RU2850849C1 - Method and system for selecting productive areas in non-oil deposits - Google Patents

Abstract

FIELD: method and system for identifying productive areas in oil-bearing deposits.

SUBSTANCE: method involves the selection of core research data and geophysical survey data from wells. Then, based on the data obtained, the organic carbon content, total porosity and their limit values are determined on the basis of the collected data. After that, oil-bearing intervals of deposits are identified based on the limit value of organic carbon content. Next, based on the limit value of total porosity, potentially productive intervals in oil-bearing deposits are identified. Geochemical studies of the core are also carried out as part of the method. After that, based on the data from the geochemical studies, the dependence of hydrocarbon yield on organic carbon content and the dependence of the oil saturation index are constructed separately for each lithotype and for each stratigraphic stage. After that, volumetric maps are constructed based on the obtained dependencies. Based on the values of geological parameters, geochemical parameters, geomechanical parameters, and formation pressure, at least one productive area is identified in the source rocks. Then, a well is drilled within the of the identified productive area using oil production equipment, resulting in an increased oil flow in the drilled well.

EFFECT: increase in the accuracy and reliability of identifying potentially productive intervals in order to increase the volume of oil produced from the field, including in domanic deposits.

22 cl

Description

Field of technology

[0001] The present invention relates to the oil industry and is used to identify productive zones of oil source deposits for their subsequent development.

State of the art

[0002] To determine whether developing a particular deposit for oil production is feasible, a preliminary assessment of the oil reserves at the field is often conducted. Several methods exist for this assessment. Each has its own advantages and limitations: volumetric methods, area calculation methods, gravity and magnetic measurements, mathematical modeling, historical data, instrumental methods, and others. Individually, these methods are insufficiently accurate or too labor-intensive, and therefore are often combined to increase the reliability of the assessment.

[0003] However, currently known combinations do not allow for an individual approach to each individual field and do not define criteria for identifying productive zones. In turn, identifying productive zones of oil source deposits is a necessary preliminary step in oil production, ensuring optimal and sustainable field operation. This maximizes production yield, reduces risks, and ensures more sustainable and successful operation of the oil production enterprise.

[0004] A method for determining the characteristics of an oil reservoir is known that combines stratigraphic modeling and geostatistics and is disclosed in application No. CN 116629023 A (published on August 22, 2023; IPC: G06F 30/20; G06F 111/04; G06F 111/08; G06F 113/08). The method includes the steps of using seismic data to create a structural model; integrating the data and key parameters and performing direct reservoir modeling to obtain an SFM mudstone model of the target reservoir. The SFM mudstone model is then adopted as a constraint and a geostatistical method is used to create a lithological model calibrated taking into account the constraints of the SFM mudstone model. Based on core data and log curve shape, sedimentary microfacies types are identified and separated in the lithological model, a coherent sedimentary microfacies analysis is performed, and a planar sedimentary microfacies distribution diagram is obtained. Microfacies modeling is then performed in the sandstone region using a three-dimensional sedimentary microfacies trend body, converted from the planar sedimentary microfacies distribution diagram, as a constraint to obtain the sedimentary microfacies model. Porosity, permeability, and oil saturation models are then created using a variation function. This method can significantly improve the accuracy of reservoir characterization and is particularly suitable for oil fields with a small number of wells or low-resolution seismic data.

[0005] A disadvantage of this technical solution is that reservoir modeling is performed using seismic data. However, if the field is located in an area of low seismic activity or has a complex geological structure, seismic data may be less informative or require more in-depth analysis. Therefore, the described methodology is suitable only for a limited number of fields with high-resolution seismic data. In addition, in the analog, porosity, permeability, and other rock properties are determined based on a model diagram of sedimentation microphases. Knowing the characteristics of the microphases in various sections of the field, the analog simulates the flow of oil, gas, and water. However, maps with a volumetric display of productive zones are not constructed in the analog, resulting in a relatively low accuracy in determining the location of well drilling. Moreover, the disclosed solution does not utilize the results of basin modeling, which would take into account geostatic processes, tectonic changes, and the development of the sedimentary cover, to predict fluid migration.

[0006] Also known is a method for localizing promising zones in oil source strata, protected by invention patent No. RU 2762078 C1 (published on 15.12.2021; IPC: G01V 1/28; G01V 1/30; G01V 11/00; E21B 47/06; E21B 47/07). The proposed method involves obtaining Poisson's ratio and Young's modulus values for the source formation and the overlying and underlying seals. These Poisson's ratio values are averaged over the thickness of the source formation intervals and the overlying and underlying seals. Their effective values are determined for each well interval, and maps of Poisson's ratio values for the source formation and the overlying and underlying seals are constructed. The brittleness index is calculated for the source formation interval, brittle zone intervals with a brittleness index ≥0.5 are identified, summed, the total brittle zone thicknesses are determined for each well, and a map of the brittle zone thicknesses of the source formation is constructed. Laboratory pyrolysis of core samples is used to determine the kerogen type, its kinetic spectrum, the thermal history of the source formation, and the subsidence history of its roof from its formation to its present state. 3D seismic and geophysical data are used to construct a map of the current source formation roof depth and the total thickness of the source formation and the overlying and underlying seals. Geophysical data are used to construct rock density maps of the source formation, overlying and underlying seals, and a map of current reservoir temperatures for the source formation. A map of kerogen concentration in the source formation under paleoenvironments is also constructed based on laboratory core pyrolysis data. The correspondence between the kerogen kinetic spectrum and the thermal history of the source reservoir is verified using numerical modeling of the kerogen thermal decomposition kinetics based on the previously determined thermal history of the source reservoir. Laboratory pyrolysis of kerogen that has undergone the thermal history modeling stage is then numerically modeled, comparing the calculated values of the kerogen's generation potential with similar values calculated based on laboratory core pyrolysis data for specific wells. Numerical modeling of pore pressure changes in the source reservoir is performed, and a map of potential pore pressure in the source reservoir is constructed. Based on the map of the current depths of the roof of the oil source formation and the map of the potential pore pressure in the oil source formation, a map of the calculated coefficients of pore pressure anomaly in the oil source formation is constructed, and it is jointly processed with the map of the thicknesses of the brittle zones of the oil source formation by ranking the intervals of the values of the calculated coefficients of pore pressure anomaly into at least 3 ranges and the thicknesses of the brittle zones into at least 3 ranges. The maps of the calculated coefficients of pore pressure anomaly in the oil source formation and the thicknesses of brittle zones of the oil source formation are recalculated into the corresponding maps of discrete indices by replacing the actual values of the parameters with the values of the discrete index for the corresponding intervals in the order of increasing their values, wherein the most promising zones are considered to be those in which the conditions for achieving the maximum values of pore pressure anomaly and the thicknesses of brittle zones in the oil source formation are simultaneously met, with a further decrease in the discrete index of prospectivity as the values of the ranges of anomalous pore pressure and the thicknesses of brittle zones that intersect on the maps of the calculated coefficients of pore pressure anomaly in the oil source formation and the thicknesses of brittle zones of the oil source formation decrease, and on the basis of this condition a discrete map of prospective zones is constructed, ranked by priority of prospectivity, where the maximum discrete index of prospectivity corresponds to the most promising zones.

[0007] The first drawback of the above-mentioned technical solution is that the zones are displayed discretely on the maps. Continuous maps typically provide a more detailed representation of the distribution of parameters in geological formations than discrete maps. Continuous maps display data as gradients or smooth transitions between values, which allows for a more precise determination of the position and characteristics of the zones of interest. Therefore, this method does not provide a high degree of accuracy in identifying promising zones. Moreover, in the analog, the localization of promising zones in source rocks is carried out using the calculated coefficient of anomalous potential pore pressure (APPP) and depth-dependent fragility in the source rocks. These coefficients, in turn, are highly arbitrary, and a significant contribution to the APPP is made by the lithological and mineral composition of the source rock. The descriptive section only provides thicknesses, and there is no descriptive section on the lithological composition in the APPP and high fragility zones. This does not allow for a complete analysis. This approach needs to be combined with other studies.

[0008] Another known method is a method for determining heterogeneity characteristics in shale production and reserves in situ according to patent No. US 11834947 B2 (published 05.12.2023; IPC: E21B 43/30; E21B 49/00; E21B 49/08). The method includes the following steps: creating an in-situ logging interpretation model with parameter generation and reservation based on lithofacies-lithofacies-well connectivity and completing the interpretation of one well; creating an in-situ 3D seismic data interpretation model with parameter generation and reservation using seismic well connectivity; creating a spatial structure of a group of layers in situ based on the connectivity of lithofacies, wells and seismic data and creating a structure of spatial distribution trends of small layers of a shale formation using a 3D visualized vertical well comparison; and the implementation of accurate 3D in-situ characterization of shale resources and reserve performance parameters using lithofacies-well-seismic data coupling based on the creation of a dual-control seismic-lithofacies parameter field. The present invention combines in-situ technology with shale logging, interpretation of seismic generation and reserve parameters, and the creation of a 3D grid model of small shale layers. This accurately describes the heterogeneity of the TOC content and porosity values of shale, oil, and gas in 3D space and provides reliable technical support for the exploration and development of shale oil and gas.

[0009] The first drawback of the described method is that it relies on a three-dimensional model for interpreting the seismic porosity of a shale gas field, constructed using a neural network. The neural network was tuned using a single well, and source deposits can undergo significant spatial variations in lithology and TOS carbon content. Therefore, this approach should be considered very approximate, and its use is limited. Also, in accordance with the above-described technical solution, the heterogeneity characteristics of source shale deposits are determined. These deposits are thin-layered condensed sediments, and therefore their layer thickness typically does not exceed a few centimeters. In such conditions, where thin layers with high and low TOS contents alternate, the use of seismic surveys to create seismolithofacies is questionable, as this will coarsen the lithosection. The limitations of seismic surveys are several meters. Estimating the potential of oil source rocks will be approximate, and this approach should be considered a first step in selecting areas for further, more in-depth study. Additionally, the analog performs a point-by-point interpretation of geophysical curves, which provides a representation of the wellbore section at 0.1-0.2 m intervals, providing a fairly detailed representation of geophysical parameters. However, the challenge with point-by-point interpretation lies in obtaining an integral curve (essentially an interpolation of values), as geophysical instruments have limitations, and their capabilities for studying thicknesses begin at 10 cm or more. Point-by-point interpretation can in some cases underestimate or overestimate the true reservoir characteristics, which may impact subsequent operational calculations. Furthermore, the diagram has a grid structure, meaning it is drawn discretely. Continuous maps typically provide a more detailed representation of the distribution of parameters in geological formations than discrete maps. Continuous maps display data as gradients or smooth transitions between values, allowing for a more precise determination of the location and characteristics of zones of interest. Therefore, this method does not provide high accuracy in identifying promising zones. Furthermore, reservoir modeling is performed using seismic data. However, if a field is located in an area of low seismic activity or has a complex geological structure, seismic data may be less informative or require more in-depth analysis. Therefore, this method is only suitable for a limited range of fields.

[0010] Furthermore, a method for geophysical forecasting of an unconventional zone favorable for oil and gas production is known, described in application No. CN 112363226 A (published 12.02.2021; IPC: G01V 1/50). This method includes the following steps: (1) drilling, coring and logging the entire well section; (2) conducting geological tests and geochemical measurements in closed spaces; (3) conducting acoustic laboratory measurements; (4) creating a physical model of the rock and collecting VSP; (5) processing the "three maxima" seismic data under the guidance and constraints of the VSP and the results of direct modeling; (6) performing geophysical direct modeling and performing inverse forecasting based on five parameters, including TOC, Ro, shale porosity, pore pressure and shale brittleness; and (7) a comprehensive analysis and comparison to determine the favorable zone for shale oil exploration. The method is primarily based on geophysical means, enables targeted prediction of five indicators of the favorable zone for shale oil exploration, has a qualitative-quantitative transition, and is of reference value for unconventional oil and gas (shale oil) exploration and related scientific research.

[0011] First, in the analog, reservoir modeling is performed using VSP seismic data. These studies are limited in area, their radius covers the first kilometers, and high-quality studies are limited to a radius of 2 km, after which the error increases significantly. Therefore, the assessment of the oil source rock potential will be local in nature. This method is convenient for assessing an already drilled well and selecting an interval for study in this well. Moreover, if the field is located in an area of low seismic activity or has a complex geological structure, seismic data may be less informative or require more in-depth analysis. Therefore, the described method is suitable only for a narrow range of fields. Secondly, it is proposed to use TOS (carbon content) to distinguish oil source rocks from the general strata. Geochemical parameters obtained during pyrolysis can contain errors (sometimes significant), therefore, using only one parameter can lead to an incorrect assessment of the oil source rock potential. To estimate the oil-generating potential of source rocks based on geochemistry, it is necessary to confirm the pyrolysis results with additional studies, such as the oil productivity index (OPI), oil saturation index (OSI), or their input data: the oil (bitumen) content in the rock (S1), the oil potential of the kerogen remaining at the time of sample analysis (S2), and their sum (S1 + S2). Furthermore, the analog lacks a description of how to construct a volumetric map displaying the specific location of the productive zone by area and depth. This reduces the accuracy of determining the specific location for well drilling.

[0012] A technical solution protected by patent No. RU 2602424 C1 (published 20.11.2016; IPC: G01V 5/10; E21B 47/00) is also known. In the method for calculating hydrocarbon reserves in domanik deposit reservoirs, geophysical surveys are carried out in existing wells passing through intervals of domanik deposits. The method of geophysical surveys is the method of pulsed spectrometric neutron gamma-ray logging. As a basis for comparison in determining productive intervals, data from the method of pulsed spectrometric neutron gamma-ray logging of a well perforated in the interval of domanik deposits, in which hydraulic fracturing was carried out and an industrial oil flow rate was obtained, are used. In addition to the method of pulsed spectrometric neutron gamma-ray logging, when calculating the obtained data, data from other geophysical survey methods are additionally used.

[0013] In the above-described method for determining productive intervals, the data from the pulsed spectrometric neutron gamma-ray logging method of a well perforated in an interval of Domanik deposits, in which hydraulic fracturing was performed and a commercial oil flow rate was obtained, are compared with the data from the same method in the studied area. Therefore, in the context of this invention, specific criteria for identifying Domanik-type oil source rocks are not defined. In view of this, the determination of productive intervals using this method is less accurate, since the determination solely on the basis of comparison, and not according to clear criteria, can lead to the omission of such intervals, i.e., to false conclusions regarding their productivity or non-productivity. In addition, in the analog, only a potentially productive interval is determined, i.e., it is determined at what depth in a particular well it is possible to obtain the best oil flow in this particular well. In this case, the distribution of potentially productive zones over an area is not determined. Thus, when using this method, it is necessary to drill a well each time (or use existing wells). However, this method does not indicate where a well should be drilled to obtain oil flow. Consequently, this method cannot ensure optimal flow. The analog also utilizes spectrometric pulse gamma logging (SPG), which allows radioactivity to be separated into three components: K (potassium), Th (thorium), and U ^-238 (uranium), as well as to determine lithology, porosity, and permeability. In this case, it is proposed to isolate source rocks from the Domanik formation section, taking into account the uranium component. The problem with SPG is its limitation on rock density; the denser the rock, the greater its error. This method is recommended for use in sediments with a porosity of at least 6-7%, while source rocks are very dense, with porosity typically not exceeding 2.5-3.0%. Under such conditions, the identified source rock thicknesses will be approximate. The second gamma-ray logging method proposed for use in the analog measures total radioactivity. This method is often unable to distinguish between source rocks and carbonate-clay rocks, where resistivities may be comparable. Overall, the resource estimation solution described above does not take into account geochemical parameters that could clarify the potential of source rocks and their thickness. Resource estimates using this method should be considered tentative. However, there is a risk of incorrectly selecting the well interval for exploration.

The essence of the invention

[0014] The objective of the present invention is to create and develop a system and method for identifying productive areas in oil source deposits, ensuring increased accuracy in identifying such areas, including in Domanik deposits.

[0015] This problem is solved by the claimed invention by achieving the following technical result: increasing the accuracy and reliability of identifying potentially productive intervals in order to increase the volume of oil produced from a field, including in Domanik deposits. The claimed technical result is achieved, among other things, by:

• the possibility of identifying potentially productive intervals;

• identification of potentially productive intervals based on boundary values;

• identification of oil source rocks based on the boundary value of organic carbon content substantiated by pyrolysis data;

• distribution of potentially productive intervals across the area, providing the possibility of localizing productive areas;

• construction of geochemical dependencies separately for each stratostage, which made it possible to construct volumetric maps for localizing productive zones;

• the ability to construct volumetric models and maps displaying the specific location of the productive zone in terms of area and depth.

[0016] A more complete technical result is achieved by a method for identifying productive areas in oil source sediments. According to this method, core study data and wellbore geophysical survey data are first collected. Then, based on the obtained data, the organic carbon content, total porosity, and their boundary values are determined based on the collected data. Following this, the oil source intervals of the sediments are identified based on the boundary value of the organic carbon content. Next, potentially productive intervals in the oil source sediments are identified based on the boundary value of the total porosity. Geochemical studies of the core are also conducted as part of the method. Following this, the dependences of hydrocarbon yield on the organic carbon content and the dependence of the oil saturation index are constructed separately for each lithotype and for each stratostage, based on the geochemical study data. Three-dimensional maps are then constructed based on the obtained dependences. Based on geological, geochemical, geomechanical, and reservoir pressure parameters, at least one productive zone is identified in the oil source sediments. A well is then drilled within the identified productive zone using oil production equipment, resulting in increased oil flow in the drilled well.

[0017] The stage of collecting core and well logging data is necessary for the formation of a database of laboratory core and well studies for their subsequent processing, the calculation of the necessary parameters on their basis, as well as the further identification of oil source rocks and their potentially productive intervals (PPI). Based on the boundary value of the organic carbon content, intervals that are oil source rocks are identified in the sediment, and among them, potentially productive intervals are identified based on the previously calculated boundary value of the total porosity. Thus, it becomes possible to identify PPI in sediments with the greatest accuracy and reliability, without spending a large amount of time and resources on conducting research. Plotting the dependences of hydrocarbon yield on the organic carbon content and the dependence of the oil saturation index separately for each lithotype and for each stratostage based on geochemical research data makes it possible to determine promising areas based on these parameters by distributing them throughout the volume. Moreover, the fact that these relationships are constructed separately for each lithotype and stratostage allowed us to obtain relationships with a high correlation coefficient. Identifying productive zones in oil source sediments simultaneously based on geological parameters, geochemical parameters, geomechanical parameters, and reservoir pressure significantly increases the accuracy of such determination, and drilling a well within the identified zone significantly increases oil inflow.

[0018] A value greater than 0.8% may be taken as the boundary value for organic carbon content, and a value of 2.3% may be taken as the boundary value for total porosity. These boundary values were determined for the Domanik deposits in the territory of the Mukhanovo-Erokhovsky trough of the Volga-Ural oil and gas province.

[0019] To identify productive areas, the following may be taken into account: (1) the effective thickness of the potentially productive interval and the effective total porosity as geological parameters; (2) the organic carbon content and the oil saturation index as geochemical parameters; and (3) the brittleness index as a geomechanical parameter.

[0020] Effective total porosity can be defined as the difference between the total porosity and the sum of the volumes of bitumen and bound water. This allows for the identification of zones with open pore space containing only light hydrocarbons.

[0021] The oil saturation index can be defined as the mass fraction of the total free hydrocarbons. This allows for the identification of rocks that accumulate light hydrocarbons.

[0022] The brittleness index can be calculated using acoustic and density logging, which allows for the separation of pure carbonates from siliceous rocks and the identification of favorable zones for hydraulic fracturing (HF).

[0023] Reservoir pressure can be determined based on measurements of the pressure buildup curve, pressure decline curve, level buildup curve, and reservoir pressure. This allows for the identification of hydrocarbon generation and localization zones, as well as the selection of priority zones for hydraulic fracturing.

[0024] During the production zone identification stage, the following steps can be taken. Initially, individual maps can be constructed based on porosity, open porosity of the formations, and their geological, geochemical, and geomechanical parameters. Subsequently, a comprehensive productivity parameter can be calculated based on effective porosity, effective open porosity of the formations, and their geological, geochemical, and geomechanical parameters. As a result, a map can be constructed of the distribution of the comprehensive parameter across the volume of oil source deposits. This will allow for the simultaneous consideration of zones with increased oil generation potential and those most favorable for hydraulic fracturing, which will significantly increase oil inflow.

[0025] When calculating the complex parameter, the following weights can be used for each of the parameters: 15-25% for effective porosity; 25-35% for effective open porosity; 15-25% for the oil saturation index; 5-15% for the brittleness index; and 15-25% for the formation pressure. In this case, those areas in which the complex parameter is from 0.7 to 1.0 conventional units (c.u.) can be identified as productive areas. These values correspond to the highest average resource density in the area, and therefore drilling a well in the specified zone will significantly increase the oil inflow.

[0026] The claimed technical result is also achieved by a system for identifying productive areas in oil source sediments. The system includes at least one device for core sampling, at least one measuring device for core analysis, at least one measuring device for conducting geophysical studies in wells, at least one measuring device for conducting geochemical studies in wells, oil production equipment for drilling wells in the identified productive areas, and at least one processor. The processor is configured to: (1) obtain core analysis data and geophysical study data in wells; (2) determine criteria for identifying oil source sediments and potentially productive intervals of oil source sediments in the form of boundary values; (3) determine organic carbon content and total porosity; (4) identifying oil source sediment intervals based on the boundary value of organic carbon content; (5) identifying potentially productive intervals in oil source sediments based on the boundary value of total porosity; (6) constructing the dependence of hydrocarbon yield on the organic carbon content and the dependence of the oil saturation index separately for each lithotype and for each stratostage; (7) constructing volumetric maps based on the obtained dependences; (8) identifying at least one productive area in the oil source sediments based on the values of geological parameters, geochemical parameters, geomechanical parameters and formation pressure.

[0027] The processor may be configured to take into account the following parameters when identifying productive ones: the effective thickness of the potentially productive interval and the effective total porosity as geological parameters; the organic carbon content and the oil saturation index as geochemical parameters; and the brittleness index as a geomechanical parameter.

[0028] The processor may also be further configured to determine the effective total porosity as the difference between the total porosity and the sum of the volumes of bitumen and bound water. This allows for the identification of zones with open pore space containing only light hydrocarbons.

[0029] In addition, the processor may be further configured to determine the oil saturation index as the mass fraction of the sum of free hydrocarbons. This allows for the identification of rocks that accumulate light hydrocarbons.

[0030] The system may further include a device for conducting acoustic logging and a device for conducting density logging. The processor may be configured to determine a brittleness index based on data obtained from the device for conducting acoustic logging and the device for conducting density logging. This allows for the separation of pure carbonates from siliceous rocks and the identification of favorable zones for hydraulic fracturing.

[0031] The system may also further include devices for measuring a pressure buildup curve, a pressure decline curve, a level buildup curve, and a formation pressure. The processor may be configured to determine formation pressure based on data obtained from the devices for measuring the pressure buildup curve, the pressure decline curve, the level buildup curve, and the formation pressure. This allows for identifying hydrocarbon generation and localization zones, as well as selecting priority zones for hydraulic fracturing.

[0032] The processor may also be configured to identify productive areas by: (1) constructing individual maps based on porosity, open porosity of formations, and their geological, geochemical, and geomechanical parameters; (2) calculating a complex productivity parameter based on effective porosity, effective open porosity of formations, and their geological, geochemical, and geomechanical parameters; (3) constructing a map of the distribution of the complex parameter by the volume of oil source deposits. This will allow for the simultaneous consideration of zones with increased oil generation potential and zones most favorable for hydraulic fracturing, which will allow for a significant increase in oil inflow.

[0033] The processor may be configured to calculate a complex parameter using the following weight for each of the parameters: 15-25% for effective porosity; 25-35% for effective open porosity; 15-25% for the oil saturation index; 5-15% for the brittleness index; and 15-25% for the formation pressure. In this case, the processor may select as productive areas those areas in which the complex parameter is from 0.7 to 1.0 conventional units (c.u.). These values correspond to the highest average resource density in the area, and therefore drilling a well in the specified zone will significantly increase the oil inflow.

Detailed description of the invention

[0034] In the following detailed description of the embodiment of the invention, numerous implementation details are provided to provide a clear understanding of the present invention. However, it will be obvious to one skilled in the art how the present invention can be used with or without these implementation details. In other instances, well-known methods, procedures, and components have not been described in detail in order not to unnecessarily obscure the features of the present invention.

[0035] Furthermore, it is clear from the above disclosure that the invention is not limited to the embodiment shown. Numerous possible modifications, changes, variations, and substitutions of non-essential features of the invention, while preserving the essence and form of the present invention, are obvious to specialists skilled in the subject matter.

[0036] According to the method for identifying productive areas in oil source sediments, core study data and well logging data are first collected. Then, based on the data obtained, the organic carbon content, total porosity, and their boundary values are determined based on the collected data. After this, the oil source intervals of the sediments are identified based on the boundary value of the organic carbon content. Next, potentially productive intervals in the oil source sediments are identified based on the boundary value of the total porosity. Geochemical studies of the core are also conducted as part of the method. Afterwards, based on the geochemical study data, dependencies of the hydrocarbon yield versus the organic carbon content and the dependence of the oil saturation index are constructed separately for each lithotype and for each stratostage. After this, volumetric maps are constructed based on the dependencies obtained. Based on the values of geological parameters, geochemical parameters, geomechanical parameters, and formation pressure, at least one productive area is identified in the oil source sediments. Then a well is drilled within the allocated productive area using oil production equipment, resulting in an increased oil flow in the drilled well.

[0037] The stage of collecting core and well logging data (hereinafter referred to as GIS) is necessary for the formation of a database of laboratory core and well studies for their subsequent processing, the calculation of the necessary parameters on their basis, as well as the further identification of oil source rocks (hereinafter referred to as OSR) and potentially productive intervals (hereinafter referred to as PPI) of OSR. Such data may include reservoir properties, the mineralogical composition of rocks, the results of geochemical studies, information on depths before and after core depth matching in such a way that each core sampling point of the wells under consideration is represented by a complete set of the necessary data. From the reservoir properties, data on liquid porosity (rock porosity determined by the liquid-wall saturation method), open porosity, permeability, and other parameters may be collected. Among the mineral compositions of rocks, data may be collected on the percentage of quartz, feldspars (plagioclase, potassium feldspar), carbonate minerals (calcite, dolomite, siderite), phosphate minerals (apatite), sulfide minerals (pyrite), clay minerals (muscovite/illite, smectite, chlorite, kaolinite, etc.), as well as the percentage of radioactive substances by weight. Geochemical studies may also include pyrolysis data, such as residual generation potential before and after extraction, the temperature of maximum hydrocarbon yield, organic carbon content, and other data.

[0038] It is also important to note that data collection may involve both the measurement of core and well logging data using dedicated measuring devices, as well as the extraction of necessary data from a pre-prepared database. Furthermore, existing data extracted from the database may be combined with data from dedicated studies.

[0039] The above data are necessary, in particular, to determine the organic carbon content, total porosity, and their boundary values based on the collected data. Based on the boundary value of organic carbon content, intervals in the sediment that are considered oil source intervals are identified, and among these, potentially productive intervals are identified based on the previously calculated boundary value of total porosity. Thus, it becomes possible to identify PPI in the sediments with the greatest accuracy and reliability, without spending a large amount of time and resources on conducting research.

[0040] The allocation of PPI can be carried out as follows.

[0041] Based on the obtained data (core study data and well logging data), a set of petrophysical relationships (e.g., of the CORE-CORE type), i.e., relationships in which core data are also compared with core data) can be constructed to calculate the pyrolytic parameters after extraction. After this, a mineral-component model of the rocks can be constructed. Next, based on the relationships (e.g., the aforementioned CORE-CORE relationships), criteria for identifying oil source sediments and potentially productive intervals of oil source sediments can be determined in the form of boundary values. After this, a system of petrophysical equations and coefficients for the petrophysical equations can be determined. Then, the mineral-component model can be reconstructed in the well. After this, based on the mineral-component model reconstructed in the well, the organic carbon content and total porosity can be determined (calculated by solving the system of equations). As a result, the boundary value of organic carbon content can be used to identify oil source intervals of deposits, and the boundary value of total porosity can be used to identify potentially productive intervals in oil source deposits.

[0042] The boundary value of the organic carbon content at which a deposit can be considered oil source may be determined on the basis of pyrolysis data by plotting the dependence of the rock's generation potential on the organic carbon content in the rock. The calculation of this boundary value can be carried out by determining what organic carbon content in the rock corresponds to the value of the generation potential for oil source rocks, equal to 2 mg HC/g of rock and being the value generally accepted in the industry at the present time, by which rocks can be divided into oil source and non-oil source rocks. Thus, starting from this value of organic carbon content in the rock and above, the interval is considered oil source. Generation potential values less than 2 mg HC/g characterize rocks whose organic matter, if it can be realized in the generation of hydrocarbons, is then in quantities that are of no practical interest from the standpoint of hydrocarbon production. The dependence of the generation potential of a rock on the content of organic carbon in the rock based on pyrolysis data can be constructed using a correlation method through statistical processing and the construction of a trend line.

[0043] The value of total porosity, by which the PPI is isolated, can be determined by the nuclear magnetic resonance method (hereinafter referred to as NMR). Based on the NMR data, the average porosity values before and after extraction can be calculated for the NMP intervals. It is then possible to construct a dependence of the total porosity (Kp _NMR.total ), determined by NMR, on the open porosity (Kp _open ) of the form Kp _NMR.total = a Kp _open +b. Assuming that the PPI have an open porosity > 0, with an open porosity equal to 0, the boundary value for the total porosity is calculated Kp _{NMR.total.gran} =b.

[0044] In this case, the most promising reservoirs and their thicknesses in the oil source strata section should be identified according to the boundary criterion of organic carbon content C _org > 0.8/1.0 (0.8%) and the boundary value of porosity K _p.total = 2.3%. These parameters were determined in accordance with the specified method for the deposits of the Volga-Ural oil and gas province in the territory of the Mukhanovo-Erokhovsky trough. In this case, the greatest potential for organic carbon content is observed at C _org > 5.1%.

[0045] Construction of hydrocarbon yield dependencies (S1, S2, S1+S2) relative to the organic carbon content separately for each lithotype (siliceous, carbonate-siliceous, siliceous-carbonate, carbonate and mixed siliceous-carbonate-clayey) and for each stratostage based on geochemical research data allows us to identify promising areas based on these parameters by distributing them throughout the volume. Moreover, the fact that these dependencies are constructed separately for each lithotype and stratostage made it possible to obtain dependencies with a high correlation coefficient (not less than 75%). In this case, it is preferable to select as promising zones those in which the organic carbon content is from 8%. Moreover, at this stage a volumetric map or 3D model of the hydrocarbon yield distribution relative to C _org in the oil source strata can be constructed.

[0046] Also, based on geochemical parameters for each lithotype, oil saturation index dependencies are constructed (OSI = S1/C _org * 100%). This parameter is typically used to select a promising interval for well drilling. Within the framework of the present invention, the scope of geochemical studies allows for the OSI to be distributed across all horizons of the oil source strata with their own dependencies.

[0047] Basin modeling results for _Tmax (maximum temperature, indicating zones of maximum heating) can also be loaded into the map or 3D model. This allows for qualitative monitoring of paleobasin heating zones and determination of kerogen maturity. Basin modeling data can also be used in the model as parameter distribution trends. Based on the _Tmax distribution within a territory and section, mesocatagenesis zones can be identified, including the most promising MK3-MK4 zones, where hydrocarbon yield is highest, and MK1-MK2 zones, where source rock production is still in its early stages. In this case, the MK1-MK3 catagenesis zones are those in which _Tmax lies in the range from 430°C to 465°C (the productivity index OPI=S1/(S1+S2) is equal to 0.1 to 0.4 conventional units), and the MK4-MK5 catagenesis zones are those in the range from 457°C to 530°C (the OPI is equal to 0.4 to 1.0 conventional units). In addition to _Tmax , lithofacies maps, paleogegographic maps, depression boundaries, etc. can also be loaded.

[0048] As a result of completing the above-described steps, volumetric distribution maps are obtained, on which at least one productive area in the oil source sediments is identified based on the values of geological parameters, geochemical parameters, geomechanical parameters, and reservoir pressure. The determination of productive areas in the oil source sediments simultaneously based on the values of geological parameters, geochemical parameters, geomechanical parameters, and reservoir pressure allows for a significant increase in the accuracy of such determination, and drilling a well within the identified area allows for a significant increase in oil inflow.

[0049] To identify productive areas, the following can be taken into account: (1) the effective thickness of the potentially productive interval and the effective total porosity as geological parameters; (2) the organic carbon content and the oil saturation index as geochemical parameters; and (3) the brittleness index as a geomechanical parameter. This allows for the integration of various parameters in order to localize promising oil source rocks. Geological criterion (effective thickness, porosity), geochemical criterion (Corg, OSI), geomechanical (brittleness index) and in-situ pressure. The integration of parameters allows for the identification of promising zones in a 3D geological model in three dimensions and the construction of maps with the localization of promising areas. In this case, these promising areas are assessed from several aspects at once in the context of oil production.

[0050] The effective oil-saturated thickness ( _EOT ) is the portion of the section with open porosity >0. It is determined by the criterion K _p.total > 2.3%. Below the limiting value of K _p.total, the voidage in the rock is concentrated in closed pores. In geological terms, this characterizes the effective oil-saturated thickness. This parameter is determined directly during the identification of the EOT. Sections with _EOT >0 are considered promising.

[0051] The effective total porosity ( _ETP ) allows for the identification of zones with open pore space containing only light hydrocarbons. This parameter is determined using a petrophysical model (PFM) created based on core and well logging data, as the difference between the total porosity and the sum of the volumes of bitumen in the rock and bound water. It can also be calculated using a mineral component model constructed on petrophysical dependencies as the sum of lost hydrocarbons (porosity by cylinders before extraction) and S1 (pyrolysis). The effective total porosity indicates mobile hydrocarbon reserves. Areas with an average _ETP > 2.3% are considered promising.

[0052] The oil saturation index is calculated as OSI = S1 * 100/C _org . The value of this parameter can be obtained from pyrolysis data, where S1 is the mass fraction of total free hydrocarbons (mg HC/g rock). OSI> 100 indicates rocks accumulating light hydrocarbons; intervals with such rocks are considered potentially productive. It is important to note that for Domanik deposits, the limiting OSI value may be below 100. In particular, promising areas may include areas with a maximum OSI value of 80.

[0053] The fragility index (or Young's modulus) can be calculated using acoustic and density logs (DTp, DTs, GGKp) for reference wells, with subsequent extension of the synthetic model through a mineral-component model. Application of Young's modulus allows for the separation of "pure" carbonate and siliceous rocks to identify favorable zones for hydraulic fracturing. It is important to note that areas with lower average Young's modulus values (less than 60 GPa) are considered promising.

[0054] A map of the anomalous intra-formation pressure (AIRP) coefficients, constructed using the pressure buildup curve (PBC), pressure decline curve (PDC), level recovery curve (LRC), and formation pressure (P _pl ) measurements, can be used as in-formation pressure. This allows for the identification of hydrocarbon generation and localization zones, as well as the selection of priority zones for hydraulic fracturing. An excess of formation pressure over hydrostatic pressure is called anomalous formation pressure. The mechanism for the occurrence of AHRP is that, under the weight of the rocks, the rock compacts, and the porosity decreases. Due to the low compressibility of the fluid, the fluid volume in the pores remains the same, resulting in additional pressure in the pores. If a barrier is located upstream that prevents fluid migration, excess pore pressure develops in the formation. Such formations are characterized by increased porosity and decreased density. It is important to note that areas with increased abnormal pressure (coefficient greater than 1.5) are considered promising.

[0055] Temperature anomalies can also be taken into account. Thus, a map of temperature anomaly coefficients can be constructed based on measured formation temperature _Tpl . This further allows for the identification of zones of abnormally high pressure caused by heat flow from the Earth's interior, with heat flow experiencing regional variations depending on the lithology and tectonic activity in the region.

[0056] Next, at the stage of identifying productive areas, the following actions can be performed. First, individual maps can be constructed based on porosity (K _eff. ), open porosity of formations (H _eff .), as well as their geochemical and geomechanical parameters, as specified earlier. After this, a complex productivity parameter can be calculated based on effective porosity, effective open porosity of formations, and their geochemical and geomechanical parameters. As a result, a map of the distribution of the complex parameter by the volume of oil source deposits can be constructed. This will allow for the simultaneous consideration of zones with increased oil generation potential and zones most favorable for hydraulic fracturing, which will significantly increase oil inflow.

[0057] When calculating a complex parameter, the following weights can be used for each of the parameters: 15-25% for effective porosity; 25-35% for effective open porosity; 15-25% for the oil saturation index; 5-15% for the brittleness index; and 15-25% for the formation pressure. In this case, the average values of the specified weights are optimal. It is also important to note that the parameters K _p.eff. and H _eff are the most significant in the process of identifying productive zones, and therefore should have a weight that is at least no less than the weight of the other parameters.

[0058] Those areas with a complex parameter ranging from 0.7 to 1.0 conventional units (c.u.) may be identified as productive areas. These values correspond to the highest average resource density and have a high productivity indicator in the area, as a result of which drilling a well in the specified zone will significantly increase oil inflow. An average value corresponds to complex parameter indicators of 0.5-0.75, and a low value corresponds to 0.25-0.5. In the range of 0.0-0.25, the territory is not considered promising areas.

[0059] The previously mentioned construction of a complex of petrophysical dependencies can be carried out as follows.

[0060] Based on core and well logging data, a petrophysical relationship can be constructed to calculate pyrolytic parameters after extraction. A petrophysical relationship (or petrophysical relationship) is a set of interconnected graphs, curves, or equations that describe the relationship between various petrophysical parameters in geological samples or rocks. The relationship may include the following parameters and their relationships: permeability and porosity, relative permeability, density, rock resistance to electric current, ultrasonic velocities, capillary properties, and other parameters. This step is necessary for determining the parameters after extraction for those samples on which studies have not yet been conducted. This simplifies the data collection and research process, since there is no need to conduct studies twice to determine the same parameters. In particular, at this stage, the dependence of the organic carbon content after extraction (C _org.pe ) on its content before extraction (C _org.de ) can be determined as a function of C _org.pe = f (C _org.de ). They can also determine the dependence of the temperature of maximum hydrocarbon yield after extraction (T _max.pe ) on its value before extraction (T _max.de ) as a function of T _max.pe = f (T _max.de ) and the dependence of the residual generation potential of the rock after extraction (S _2.pe ) on its value before extraction (S _2.de ) as a function of S _2.pe = f (S _2.de ). Similarly, they can determine the dependencies for calculating the porosity of the rock and the density of the rock before extraction on their values after extraction.

[0061] The MCM can also be constructed based on the specified data and a set of petrophysical dependencies. Constructing the MCM allows for the correct determination of the boundary values of C _org and K _p.total . This is due to the fact that the boundary values will vary depending on the type of deposits being studied, as well as on the specific composition. For example, Domanik deposits consist mainly of calcite, quartz, and dolomite, while the proportion of clays in the rocks is insignificant. With depth, dolomitization of the rocks increases and the proportion of quartz in their composition decreases. The mineralogical composition of the Bazhenov deposits, in turn, is mainly quartz, and the role of carbonate and clay minerals is approximately equal. US deposits (in particular, Bakken, Eagle Ford) are characterized by a greater contribution of the clay component. Thus, to determine the sediment type and to subsequently calculate the boundary values based on the MCM, it is preferable that the MCM includes at least the mass content of quartz, carbonate rocks (in particular calcite and dolomite) and clay minerals in the rock.

[0062] It is important to note that any additional features of the method according to the present invention can be used either all at once, individually or in any combination. When included in the method, the described additional technical results will be achieved.

[0063] The above-described method for identifying productive areas in oil source sediments can be carried out using the system according to the present invention. The system for identifying productive areas includes at least one device for core sampling, at least one measuring device for core testing, at least one measuring device for conducting geophysical studies in wells, at least one measuring device for conducting geochemical studies in wells, oil production equipment for drilling wells in the identified productive areas and at least one processor. Moreover, the processor is configured to: (1) obtain core testing data and geophysical study data in wells; (2) determine criteria for identifying oil source sediments and potentially productive intervals of oil source sediments in the form of boundary values; (3) determine the organic carbon content and total porosity; (4) identify the oil source intervals of sediments according to the boundary value of the organic carbon content; (5) identification of potentially productive intervals in oil source sediments based on the boundary value of total porosity; (6) construction of the dependence of hydrocarbon yield on the content of organic carbon and the dependence of the oil saturation index separately for each lithotype and for each stratostage; (7) construction of volumetric maps based on the obtained dependences; (8) identification of at least one productive area in oil source sediments based on the values of geological parameters, geochemical parameters, geomechanical parameters and formation pressure.

[0064] The processor may be configured to take into account the following parameters when identifying productive ones: the effective thickness of the potentially productive interval and the effective total porosity as geological parameters; the organic carbon content and the oil saturation index as geochemical parameters; and the brittleness index as a geomechanical parameter.

[0065] The processor may also be further configured to determine the effective total porosity as the difference between the total porosity and the sum of the volumes of bitumen and bound water. This allows for the identification of zones with open pore space containing only light hydrocarbons.

[0066] In addition, the processor may be further configured to determine the oil saturation index as the mass fraction of the sum of free hydrocarbons. This allows for the identification of rocks that accumulate light hydrocarbons.

[0067] The system may further include a device for conducting acoustic logging and a device for conducting density logging. The processor may be configured to determine a brittleness index based on data obtained from the device for conducting acoustic logging and the device for conducting density logging. This allows for the separation of pure carbonates from siliceous rocks and the identification of favorable zones for hydraulic fracturing.

[0068] The system may also further include devices for measuring a pressure buildup curve, a pressure decline curve, a level buildup curve, and a formation pressure. The processor may be configured to determine formation pressure based on data obtained from the devices for measuring the pressure buildup curve, the pressure decline curve, the level buildup curve, and the formation pressure. This allows for identifying hydrocarbon generation and localization zones, as well as selecting priority zones for hydraulic fracturing.

[0069] The processor may also be configured to identify productive areas by: (1) constructing individual maps based on porosity, open porosity of formations, and their geological, geochemical, and geomechanical parameters; (2) calculating a complex productivity parameter based on effective porosity, effective open porosity of formations, and their geological, geochemical, and geomechanical parameters; (3) constructing a map of the distribution of the complex parameter by the volume of oil source deposits. This will allow for the simultaneous consideration of zones with increased oil generation potential and zones most favorable for hydraulic fracturing, which will allow for a significant increase in oil inflow.

[0070] The processor may be configured to calculate a complex parameter using the following weight for each of the parameters: 15-25% for effective porosity; 25-35% for effective open porosity; 15-25% for the oil saturation index; 5-15% for the brittleness index; and 15-25% for the formation pressure. In this case, the processor may select as productive areas those areas in which the complex parameter is from 0.7 to 1.0 conventional units (c.u.). These values correspond to the highest average resource density in the area, and therefore drilling a well in the specified zone will significantly increase the oil inflow.

[0071] It is important to note that any additional features of the system according to the present invention may be used simultaneously, individually, or in any combination. When included in the system, the described additional technical results will be achieved. Moreover, additional steps of the method may also be considered as additional functions, as well as additional or basic elements of the system.

[0072] In a particular embodiment of the present invention, the selection of productive ones can be carried out as follows.

[0073] Potentially productive intervals within source rocks may be identified first. To do this, available core and well logging information is first collected. A database of laboratory core studies is then created, including reservoir properties, rock mineralogical composition, geochemical study results, and depth information before and after core depth matching, so that each core sampling point for the wells under consideration is represented by a complete set of the necessary data specified in paragraph [0037]. Next, based on the created database, a set of petrophysical relationships is constructed for calculating pyrolytic parameters after extraction for samples on which these studies were not performed: (a) the dependence of organic carbon content after extraction on its content before extraction; (b) the dependence of the temperature of maximum hydrocarbon yield after extraction on its value before extraction; (c) the dependence of the residual generation potential of the rock (S ₂ ) after extraction on its value before extraction. From the constructed dependencies, dependencies are established for calculating the porosity and density of the rock before extraction from their values after extraction. After this, the mass content of kerogen in the rock is calculated as follows: (1) determine the reflectivity of vitrinite (Ro) is determined by the dependence on temperature; (2) calculate the proportion of carbon in kerogen ( a ) based on the dependence on the reflectivity of vitrinite ( a = 11.037Ln (Ro) + 82.013); (3) calculate the kerogen content (C _ker ) from the content of organic carbon (C _org.pe ) after extraction using the formula Next, the content of light hydrocarbons is calculated based on the content of light bitumen in the rock, expressed in mg HC/g of rock. The content of light HC in mass percent is determined by the formula: C _HC = C _{bit_l} × 0.1, where C _HC is the mass content of light hydrocarbons in %; C _{bit_l} is the value of the pyrolytic parameter in mg HC/g of rock; 0.1 is the coefficient for converting from weight ppm to weight percent. After that, the light hydrocarbons lost during core sampling are calculated by the formula C _HC = (Kp _de × ρ _n )/ρ _ob , where C _HC is the mass content of lost hydrocarbons, %; Kp. _de is the porosity before extraction, %; ρ _n is the density of oil, g/cm ³ ; ρ _ob is the bulk density of the sample, g/cm ³ . The apparent density of the rock skeleton, calculated during porosity determination by the gas-volumetric method on unextracted samples, is taken as the sample density ρ _ob . Then, the heavy hydrocarbons contained in the rock are determined as the difference between the values of the pyrolytic parameter S ₂ , determined before and after extraction. The content of heavy hydrocarbons in mass percent is determined by the formula C _yвт = ΔS ₂ × 0.1, where C _yвт is the mass content of heavy hydrocarbons; ΔS ₂ = S _2de - S _2pe , mg HC/g of rock; 0.1 is the coefficient for converting from mass ppm to mass percent. After that, the mineral components of the rock, determined by X-ray phase analysis (XRD), based on the commonality of physical properties and composition, are combined into single components of the MCM, their volume contents are summed up: (a) quartz and feldspars; (b) carbonate minerals; (c) phosphate and sulfate minerals; and (d) clay minerals. The mass content of minerals in the rock, determined using X-ray fluorescence analysis, is corrected for the presence of kerogen and its derivatives, as well as for the water content. Since the content of all components in the sample is expressed as mass percent, the correction for the mineral component content using X-ray fluorescence analysis was performed using the following formula:

Where And - respectively, the initial and corrected content of the mineral according to X-ray fluorescence analysis, % by weight; - the total content of all other rock components (including retained oil and water), the content of which was determined by other methods, in % by mass; and i is the mineral index from the list of those determined using X-ray diffraction data. After the adjustment, the model consistency condition is checked in mass percent so that the sum of the components is equal to 100% for each sample. 13. Then the transition from the mass content of all components to their volume content is performed. The adjustment of the obtained result, i.e. bringing the sum of mineral and non-mineral components of each rock sample to 100%, is performed using multiplicative coefficients. The result of the mineral-component model calculation is the volume content of the mineral components of the rock determined in the laboratory, as well as the following non-mineral components: (a) volume content of lost hydrocarbons (Kp _de ); (b) volume content of light HC in the rock (S ₁ ); (c) volume content of heavy hydrocarbons in the rock (S _2de -S _2pe ); (d) the volumetric content of bound water in the rock; and (d) the volumetric content of kerogen in the rock. Then, based on the MCM and petrophysical dependencies, the criteria for identifying oil source sediments and potentially productive intervals of oil source sediments are determined in the form of boundary values. The boundary value of the organic carbon content is determined by constructing the dependence of the generation potential on the organic carbon content and selecting the boundary value of the organic carbon content at which the value of the generation potential is equal to 2 mgHC/g. With this value, the rock can be considered as an oil source rock. The boundary value of the total porosity is determined by constructing the dependence of the total porosity of the rock, determined by the nuclear magnetic resonance (NMR) method, on the open porosity determined by helium on the rock after extraction. In this case, the value corresponding to an open porosity of 0 is selected as the boundary value of the total porosity. An open porosity equal to zero means that there are no hydrocarbons in the open pores of the rock, i.e. available for extraction during deposit development, therefore, the limiting value of total porosity can be found using this value. Then, it is necessary to determine the dependencies of these values in the rock to isolate the NMP and PPI in a specific sedimentary sequence at a specific development site. For this, it is necessary to select a system of petrophysical equations for the best restoration of the MCM obtained from the core. Systems of equations are selected based on the availability of methods in the GIS complex. After this, using the MCM, coefficients are selected to calculate the system of equations (the selection method is used to determine the values of the coefficients at which the deviations in the calculated parameters are minimal in comparison with the measured values of these parameters). Then, the lithology and porosity of the rock are determined. The result of the system calculation is the volume content of the mineral components of the rock, as well as the following non-mineral components: (a) volume content of mobile hydrocarbons (Kp); (b) volume content of kerogen in the rock; (c) volume content of carbonate in the rock; (d) volume content of silicite in the rock; (d) the volumetric content of pyrite in the rock; (e) the volumetric content of bitumen in the rock; and (g) the volumetric content of clay in the rock. Next, the transition from the volumetric content of all components to their mass content is performed. After this, lithotypes and lithology are distinguished based on the volumetric content of the components. Then the concentration of organic matter (C _org ) is calculated. For this, the volumetric content of kerogen in the rock V _ker is converted to the mass content C _ker . Next, the proportion of organic carbon C _org is calculated using the formula: C _org = 0.85 * C _ker , where 0.85 is the proportion of carbon in kerogen. Then the total porosity is determined by summing the volumetric content of the non-mineral components of the rock. According to previously established criteria (boundary values of organic carbon content and total porosity), intervals of oil source rocks (OR) are identified in the section of specific sediments in a specific study location based on the boundary value of organic carbon content, and within these intervals, potentially productive intervals (PPI) are identified based on the boundary value of porosity.

[0074] After this, the allocated PPI may be distributed by volume as follows.

[0075] Based on the data from geochemical studies, the dependences of the hydrocarbon yield (S1, S2, S1+S2) on the organic carbon content (C _org ) and the dependence of the oil saturation index (OSI=S1/C _org *100%) are constructed separately for each lithotype (siliceous, carbonate-siliceous, siliceous-carbonate, carbonate and mixed siliceous-carbonate-clayey) and for each stratostage. At this stage, a 3D model of the distribution of the specified parameters is constructed. The results of basin modeling of T _max are additionally loaded into this model. Based on the distribution of T _max within the territory and section, it is possible to establish mesocatagenesis zones, the most promising MK3-MK4, where the hydrocarbon yield is maximum. Next, volumetric maps are constructed for the following parameters: effective thickness of the potential reservoir pressure (H _eff ), effective porosity (K _p.eff ), organic carbon content (C _org ), oil saturation index (OSI), brittleness index (Young's modulus, E) and anomalous in-situ pressure (ΔP). These maps are superimposed on each other, and a complex parameter (A) is calculated at each point of the map. Calculation of the complex parameter can be carried out using the following formula: A = 20% * K _p.eff + 30% * H _eff + 20% * OSI + 10% * E + 20% * ΔP. A productive area is identified, the complex parameter (A) of which lies in the range from 0.7 to 1.0 conventional units (c.u.). This area is developed and an increased oil inflow is obtained from the wells drilled within it.

[0076] These application materials present a preferred disclosure of the implementation of the claimed technical solution, which should not be used as limiting other, particular embodiments of its implementation that do not go beyond the scope of the requested scope of legal protection and are obvious to specialists in the relevant field of technology.

Claims (67)

1. A method for identifying productive areas in oil source sediments, according to which: collect core study data, geophysical survey data in wells; determine the organic carbon content, total porosity and their limit values based on the collected data; according to the boundary value of the organic carbon content, oil source intervals of sediments are distinguished; Potentially productive intervals in oil source sediments are identified based on the boundary value of total porosity; conduct geochemical studies on core samples; based on the data from geochemical studies, a dependence of the hydrocarbon yield on the content of organic carbon and a dependence of the oil saturation index are constructed separately for each lithotype and for each stratostage; construct volumetric maps based on the obtained dependencies; based on the values of geological parameters, geochemical parameters, geomechanical parameters and reservoir pressure, at least one productive area is identified in the oil source sediments; They drill a well within the allocated productive area using oil production equipment. 2. The method according to paragraph 1, characterized in that the limiting value of the organic carbon content is taken to be more than 0.8%. 3. The method according to paragraph 1, characterized in that the limiting value of the total porosity is taken to be 2.3%. 4. The method according to paragraph 1, characterized in that in order to identify productive areas the following is taken into account: effective thickness of the potentially productive interval and effective porosity as geological parameters; organic carbon content and oil saturation index as geochemical parameters; and brittleness index and formation pressure as geomechanical parameters. 5. The method according to paragraph 4, characterized in that the effective porosity is determined as the difference between the total porosity and the sum of the volumes of bitumen and bound water. 6. The method according to paragraph 4, characterized in that the oil saturation index is determined as the mass fraction of the sum of free hydrocarbons. 7. The method according to paragraph 4, characterized in that the brittleness index is calculated using acoustic and density logging. 8. The method according to paragraph 4, characterized in that the formation pressure is determined on the basis of measurements of the pressure recovery curve, the pressure drop curve, the level recovery curve, and the formation pressure. 9. The method according to paragraph 4, characterized in that when identifying productive areas: construct separate maps based on effective porosity, effective thickness of potentially productive intervals of formations, their geological, geochemical and geomechanical parameters; calculate a complex productivity parameter based on the effective porosity, effective thickness of potentially productive intervals of formations, their geological, geochemical and geomechanical parameters; construct a map of the distribution of a complex parameter by the volume of oil source deposits. 10. The method according to paragraph 9, characterized in that when calculating the complex parameter, the following weights are used for each of the parameters: 15-25% - for effective porosity; 25-35% - for the effective thickness of potentially productive intervals; 15-25% - for the oil saturation index; 5-15% - for the fragility index; and 15-25% - for reservoir pressure. 11. The method according to paragraph 10, characterized in that productive areas are those areas in which the complex parameter is from 0.7 to 1.0. 12. A system for identifying productive areas in oil source sediments, including: at least one core sampling device; at least one measuring device for core examination; at least one measuring device for conducting geophysical surveys in wells; at least one measuring device for conducting geochemical studies in wells; oil production equipment for drilling wells in designated productive areas; and at least one processor configured to: obtaining core study data and geophysical survey data in wells; determination of criteria for identifying oil source deposits and potentially productive intervals of oil source deposits in the form of boundary values; determination of organic carbon content and total porosity; allocation of the boundary value of organic carbon content of oil source intervals of sediments; identification of potentially productive intervals in oil source sediments based on the boundary value of total porosity; construction of the dependence of hydrocarbon yield on the content of organic carbon separately for each lithotype and for each stratostage; construction of volumetric maps based on the obtained dependencies; identification of at least one productive area in oil source sediments based on the values of geological parameters, geochemical parameters, geomechanical parameters and formation pressure. 13. The system according to paragraph 12, characterized in that the processor is designed with the ability to take into account the following parameters when allocating productive ones: effective thickness of the potentially productive interval and effective porosity as geological parameters; organic carbon content and oil saturation index as geochemical parameters; and fragility index and reservoir pressure as geomechanical parameters. 14. The system according to claim 13, characterized in that the processor is configured to determine the effective porosity as the difference between the total porosity and the sum of the volumes of bitumen and bound water. 15. The system according to paragraph 13, characterized in that the processor is configured to determine the oil saturation index as the mass fraction of the sum of free hydrocarbons. 16. The system according to claim 12, characterized in that it includes a device for conducting acoustic logging and a device for conducting density logging. 17. The system according to claim 13 or 16, characterized in that the processor is configured to determine the brittleness index based on data received from the device for conducting acoustic logging and the device for conducting density logging. 18. The system according to claim 12, characterized in that it includes devices for measuring the pressure recovery curve, the pressure drop curve, the level recovery curve, and the formation pressure. 19. The system according to paragraph 13 or 18, characterized in that the processor is configured to determine the formation pressure based on data obtained from devices for measuring the pressure recovery curve, the pressure drop curve, the level recovery curve, and the formation pressure. 20. The system according to paragraph 13, characterized in that the processor is designed with the possibility of isolating productive areas by means of: construction of individual maps of effective porosity, effective thicknesses of potentially productive intervals, their geological, geochemical and geomechanical parameters; calculation of a complex productivity parameter based on effective porosity, effective thicknesses of potentially productive intervals, their geological, geochemical and geomechanical parameters; construction of a map of the distribution of a complex parameter by the volume of oil source deposits. 21. The system according to claim 20, characterized in that the processor is configured to calculate a complex parameter using the following weight for each of the parameters: 15-25% - for effective porosity; 25-35% - for effective thicknesses of potentially productive intervals; 15-25% - for the oil saturation index; 5-15% - for the fragility index; and 15-25% - for reservoir pressure. 22. The system according to claim 21, characterized in that the processor is configured to select as productive areas those areas in which the complex parameter is from 0.7 to 1.0.