CN107402411A - A Quantitative Identification Method of Algal Dolomite in Microbial Carbonate Formation - Google Patents

Abstract

The invention provides a quantitative identification method of microbial carbonate stratum algae dolomite. The method comprises the following steps: acquiring logging data of a target stratum and core data of algae dolomite; carrying out normalization processing on the logging data to obtain processed logging data; constructing an identification parameter based on the processed logging data; determining an identification cut-off value of the algae dolomite based on the rock core data of the algae dolomite and the identification parameters; and identifying the algae dolomite of the target stratum of the other non-coring wells according to the identification cut-off value of the algae dolomite. The technical scheme provided by the invention can be used for aiming at the microbial carbonatite stratum, has no geological condition and application limitation, is simple to operate, has higher lithology identification accuracy, and can meet the production requirement.

Description

A Quantitative Identification Method of Algal Dolomite in Microbial Carbonate Formation

technical field

The invention relates to a method for quantitatively identifying algal dolomite in microbial carbonate formations, and belongs to the technical fields of petroleum geological exploration and logging interpretation.

Background technique

Using well logging data to identify algal dolomite has always been a difficult problem in the world. Algal dolomite is a kind of microbial carbonate rock. Because microbial carbonate rock formations are subject to sedimentation and diagenesis (dolomitization, dissolution, and fracture development) etc.) transformation, coupled with the action of microorganisms, make the logging characteristics very complex, and it is difficult to use logging to identify different types of microbial rocks, especially the accurate identification of algal dolomite.

At present, there are few researches on algal dolomite logging identification methods in China, and no patents specifically for algal dolomite logging quantitative identification have been found. The relevant literature consulted is mainly for algal limestone logging identification. Algal limestone, such as lacustrine algal stromatolime in the Qaidam Basin, but for the lithology of algal dolomite, take the Zhendan Dengying Formation in Sichuan as an example, because the algae lamina and laminar structures are not clear, electrical imaging cannot Identification can only be identified by conventional logging data.

At present, the main method is to use neural network, cluster analysis, discriminant analysis and other identification methods. On the one hand, it is mainly aimed at the relatively coarse lithology classification at the level of rock mineral composition (Liu Weifu, fuzzy mathematics identification of carbonates in deep buried hills Lithology [J], Journal of Xinjiang Petroleum Society, 2003, 15(3): 35-39), on the one hand, it is aimed at the lithology identification for the classification of rock structure components (Liu Hong, Tan Xiucheng, Zhou Yan, etc., based on gray correlation Logging lithofacies identification of complex carbonate rocks [J], Daqing Petroleum Geology and Development, 2008, 27(1): 122-125), but there are few studies on logging identification of special lithology such as algal dolomite .

Therefore, providing a quantitative identification method for algal dolomite in microbial carbonate formations has become an urgent technical problem in this field.

Contents of the invention

In order to solve the above-mentioned technical problems, the object of the present invention is to provide a quantitative identification method for algal dolomite in microbial carbonate formations. The method is simple to operate and has high recognition accuracy.

In order to achieve the above object, the invention provides a kind of quantitative identification method of algae dolomite in microbial carbonate formation, the method comprises:

Obtain the logging data of the target formation and the core data of the algal dolomite;

performing normalization processing on the logging data to obtain normalized logging data;

Constructing identification parameters based on the normalized logging data;

Based on the core data of the algal dolomite and the identification parameters, determine the identification cut-off value of the algal dolomite;

According to the identification cut-off value of the algal dolomite, the algal dolomite in the target formation is identified.

The current existing technology involves carbonate lithofacies identification, but it has the following defects and disadvantages: 1) It has limited application to the geological conditions of the stratum; 2) The construction of formulas and parameters is cumbersome and prone to process errors. Compared with the prior art, the technical solution provided by the present invention can identify microbial rocks, that is, algal dolomite, without any geological conditions and application restrictions (even water layers can be applied). In addition, the present invention provides The technical scheme directly uses well logging curves to construct identification parameters, which is simple and convenient, and will not produce process errors.

In the above method, the log data is normalized according to the formula shown in formula 1 to obtain the processed log data

In formula 1, X represents the log data before normalization processing, X _a represents the log data after normalization processing, X _min represents the minimum value in the log data before normalization processing; X _max represents the normalized The maximum value in the log data before chemical processing. Since different well logging data have different dimensions, the normalization process on the well logging data can make the data fall within the range of 0-1.

The technical solution provided by the present invention selects representative logging data when identifying algal dolomite, and the logging data includes acoustic time difference DT, density DEN, neutron porosity CNL, natural gamma GR and deep lateral Resistivity RD.

In the above method, preferably, the normalized logging data is substituted into the calculation formula shown in Equation 2 to obtain the identification parameter ZSL

In Equation 2, ZSL represents the identification parameter, GRa represents the natural gamma after normalization processing, DENa represents the density after normalization processing, RDa represents the deep lateral resistivity after normalization processing, and CNLa represents the normalized The neutron porosity after processing, DTa represents the acoustic time difference after normalization processing.

In the technical solution provided by the present invention, the logging data is the logging curve, and the logging curve includes acoustic time difference DT, density DEN, neutron porosity CNL, natural gamma GR and deep lateral resistivity RD, and The identification parameter ZSL is constructed based on well logging data, so the identification parameter ZSL is also in the form of a curve.

The research of the present invention finds that algal dolomite has the characteristics of porosity and organic matter, and its logging characteristics usually have the characteristics of high acoustic wave time difference, high neutron porosity, low density, low natural gamma ray, and low resistivity. In order to reflect the above logging rules in the form of parameters, the technical solution provided by the present invention adopts the formula shown in formula 2 to construct the identification parameter ZSL. The thinking of the construction of the present invention is that the density, resistivity and natural gamma ray logging parameters with small logging eigenvalues are divided by the acoustic wave and neutron porosity logging parameters with large logging eigenvalues, and the identification parameter ZSL obtained in this way is , can fully highlight the characteristics of algal dolomite, and it is clearer in distinguishing algal dolomite, and the change characteristics of the identification parameter ZSL are also more obvious, achieving the purpose of quickly identifying algal dolomite.

In the technical solution provided by the present invention, after obtaining the identification parameter ZSL, it is necessary to divide the algal dolomite according to the identification cut-off value to achieve the purpose of identification; wherein, the identification cut-off value can be obtained by combining the core data and identification parameters ZSL is determined by comparison with the same depth.

In the above method, the core information includes the identification results of thin sections of the core and/or the direct observation and description results of the core. Wherein, the identification result of the thin section of the core can be obtained by grinding the core into a thin section and observing and identifying it under a microscope.

Taking Fig. 2 as an example, this figure is the identification cut-off value determined by a certain work area using the technical solution provided by the present invention, and the value is 0.2. The black part in the figure represents the identified algal dolomite, and it can be seen that the coincidence rate with the rock core is 70% % or more, the identification cut-off value is representative in this work area (that is, the cut-off value in this work area can be identified with this cut-off value). If another work area is changed, the identification cut-off value will change, and it needs to be re-calibrated to determine the core data obtained from wells in other work areas, but the calculation formula of the identification parameter ZSL remains unchanged.

In the technical solution provided by the present invention, the identification parameters can constitute a curve, which can also be referred to as an identification curve, and the core data of the algal dolomite and the identification curve are compared and calibrated. The identification cut-off value of the algal dolomite can be determined on the identification curve.

In the above method, preferably, according to the identification cut-off value of the algal dolomite, identifying the algal dolomite in the target formation includes:

When the identification parameter ≤ the identification cut-off value of the algal dolomite, it is judged that the lithology at the formation depth corresponding to the identification parameter is algal dolomite; otherwise, it is other lithology. That is, when the identification parameter ≤ the identification cut-off value of the algal dolomite, it is judged that the lithology at the formation depth corresponding to the identification parameter is algal dolomite; when the identification parameter > the identification cut-off value of the algal dolomite , judging that the lithology at the formation depth corresponding to the identification parameter is other lithology.

In the above method, the target formation may be a non-coring well formation.

Beneficial effects of the present invention:

1) The technical scheme provided by the present invention can be applied to any microbial carbonate stratum, as long as the microbial carbonate stratum has the above-mentioned 5 well logging curves, and enough coring well core data;

2) the microbial carbonatite rock formation that the technical solution provided by the present invention is aimed at, can be any geological situation, without application limitation;

3) The biggest feature of the technical solution provided by the present invention is that it is easy to operate, and the accuracy of lithology identification is relatively high, which can meet production requirements.

Description of drawings

FIG. 1 is a schematic flowchart of an identification method provided by an embodiment of the present invention;

Fig. 2 is a comparison and calibration diagram of identifying algal dolomite in well Moxi 51 core slices with the identification parameter ZSL;

Fig. 3 is a verification map of algal dolomite logging identification in Well Moxi 105.

detailed description

In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the technical solution of the present invention is described in detail below, but it should not be construed as limiting the scope of implementation of the present invention.

The Dengying Formation strata in the Sichuan Basin earthquake are a set of microbial carbonate rock deposits, which can be roughly divided into three categories: grain dolomite, grain clastic dolomite and algal dolomite (that is, algal dolomite). The algal dolomite includes layers Dolomite, dolomite, dolomite, dolomite, dolomite, clotted dolomite, etc.; the lithology of high-quality reservoirs is mainly algal dolomite, so the identification of algal dolomite by well logging is very important for finding high-quality Reservoirs are critical.

The embodiment of the present invention takes the Sichuan Basin earthquake but the Dengying Formation formation as the research object, and provides a quantitative identification method for algal dolomite in the microbial carbonate rock formation, as shown in Figure 1, which may include the following steps:

Step S101: Obtain the logging data of the target formation and the core data of the algal dolomite;

Obtain well logging data in the study area, as well as the thin-section identification results of cores from Well Moxi 51 in the research area (that is, the algal dolomite that is ground into thin slices and identified under a microscope); among them, the logging data include acoustic time difference DT, Density DEN, neutron porosity CNL, natural gamma ray GR and deep lateral resistivity RD; these logging data can be obtained by using existing technical equipment, such as CLS-3700 logging tool, CLS-5700 logging tool, etc.

Step S102: performing normalization processing on the logging data to obtain processed logging data;

The above five kinds of logging data are normalized respectively, and the processing process is as follows:

In formula 3, DT represents the acoustic time difference before normalization processing, DT _a represents the acoustic time difference after normalization processing, DT _min represents the minimum value of the acoustic wave time difference before normalization processing, and DT _max represents the normalization processing The maximum value in the pre-sonic time difference;

In formula 4, DEN represents the density before normalization processing, DEN _a represents the density after normalization processing, DEN _min represents the minimum value in the density before normalization processing, and DEN _max represents the density in the density before normalization processing the maximum value;

In Equation 5, CNL represents the neutron porosity before normalization, CNL _a represents the neutron porosity after normalization, CNL _min represents the minimum value of neutron porosity before normalization, CNL _max represents the maximum value of neutron porosity before normalization;

In Formula 6, GR represents the natural gamma before normalization processing, GR _a represents the natural gamma after normalization processing, GR _min represents the minimum value of natural gamma before normalization processing, and GR _max represents the normalized natural gamma The maximum value of natural gamma before normalization;

In Equation 7, RD represents the deep lateral resistivity before normalization, RD _a represents the deep lateral resistivity after normalization, and RD _min represents the minimum deep lateral resistivity before normalization RD _max represents the maximum value of the deep lateral resistivity before normalization.

The above-mentioned minimum and maximum values are specifically the following values in this embodiment:

_DTmin ＝43us/ft

_DTmax ＝55us/ft

_DENmin ＝2g/ ^cm3

_DENmax ＝3g/ ^cm3

_CNLmin =0

CNL _max = 1

GR _min = 6API

GR _max = 60API

_RDmin ＝220Ω·m

_RDmax ＝99990Ω·m

Step S103: Construct identification parameters based on the processed logging data;

Based on the processed logging data, according to the formula shown in Equation 2, the identification parameter ZSL is constructed (as shown in Fig. 2):

Step S104: Based on the core data of the algal dolomite and the identification parameters, determine the identification cut-off value of the algal dolomite;

According to the comparison and calibration of the thin section identification results of cores from Well Moxi 51 and the identification parameter ZSL at the same depth, the identification cut-off value of algal dolomite was determined on the identification parameters through comparison and calibration.

In this embodiment, according to the comparison result, the identification cut-off value is determined to be 0.2 (as shown in FIG. 2 ).

Step S105: Identify the algal dolomite in the target formation according to the identification cut-off value of the algal dolomite;

When the data in the identification parameters is ≤0.2, it is judged as algal dolomite;

When the data in the identification parameter is >0.2, it is judged as other lithology.

In order to verify the reliability of the identification results, taking Well Moxi 9, another coring well in the study area, as an example, referring to the above steps, the identification results are shown in Figure 3. Comparing the description and identification results of the core slices, the core slices Comparing the number 10 with the total number of slices 14, the recognition rate is more than 70%. It can be seen that when the technical solution provided by the present invention quantitatively identifies the algal dolomite in the microbial carbonate formation, the rate of recognition is quite high. High.

Claims (7)

1. A quantitative identification method of algal dolomite in a microbial carbonate formation, the method comprising: Obtain the logging data of the target formation and the core data of the algal dolomite; performing normalization processing on the logging data to obtain normalized logging data; Constructing identification parameters based on the normalized logging data; Based on the core data and the identification parameters, determine the identification cut-off value of algal dolomite; According to the identification cut-off value of the algal dolomite, the algal dolomite in the target formation is identified. 2. method according to claim 1, is characterized in that, according to the formula shown in formula 1, described logging data is carried out normalization process, to obtain the logging data after processing In formula 1, X represents the log data before normalization processing, X _a represents the log data after normalization processing, X _min represents the minimum value in the log data before normalization processing; X _max represents the normalized The maximum value in the log data before chemical processing. 3. The method according to claim 1 or 2, characterized in that the logging data includes acoustic transit time, density, neutron porosity, natural gamma ray and deep lateral resistivity. 4. method according to claim 1, is characterized in that, based on the logging data after normalization process, according to the formula shown in formula 2, builds identification parameter In Equation 2, ZSL represents the identification parameter, GRa represents the natural gamma after normalization processing, DENa represents the density after normalization processing, RDa represents the deep lateral resistivity after normalization processing, and CNLa represents the normalized The neutron porosity after processing, DTa represents the acoustic time difference after normalization processing. 5. The method according to claim 1, characterized in that, based on the core data of the algal dolomite and the identification parameters, determining the identification cut-off value of the algal dolomite comprises: The core data of the algal dolomite is compared with the identification parameters at the same depth, so as to determine the identification cut-off value of the algal dolomite. 6. The method according to claim 1, wherein, according to the identification cut-off value of the algal dolomite, identifying the algal dolomite of the target formation comprises: When the identification parameter ≤ the identification cut-off value of the algal dolomite, it is judged that the lithology at the formation depth corresponding to the identification parameter is algal dolomite; otherwise, it is other lithology. 7. The method according to claim 1, characterized in that, the core data include identification results of thin sections of the core and/or direct observation and description results of the core.