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WO2001098631A1 - Systeme de gestion de connaissances en matiere de forage base sur des cas - Google Patents

Systeme de gestion de connaissances en matiere de forage base sur des cas Download PDF

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Publication number
WO2001098631A1
WO2001098631A1 PCT/US2000/016922 US0016922W WO0198631A1 WO 2001098631 A1 WO2001098631 A1 WO 2001098631A1 US 0016922 W US0016922 W US 0016922W WO 0198631 A1 WO0198631 A1 WO 0198631A1
Authority
WO
WIPO (PCT)
Prior art keywords
drilling
formation
lithology
wellbore
knowledge
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2000/016922
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English (en)
Inventor
Alan N. Fleet
Derek H. Sleeman
Alun D. Preece
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Baker Hughes Holdings LLC
Original Assignee
Baker Hughes Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Baker Hughes Inc filed Critical Baker Hughes Inc
Priority to EP00939969A priority Critical patent/EP1297244B1/fr
Priority to PCT/US2000/016922 priority patent/WO2001098631A1/fr
Priority to AU2000254972A priority patent/AU2000254972A1/en
Publication of WO2001098631A1 publication Critical patent/WO2001098631A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions

Definitions

  • a method has been designed for storing drilling knowledge and experience in a highly structured fashion that permits the user to identify drilling cases that meet user-specified criteria and to retrieve the knowledge and experience relating to those cases. In this way the user is able to retrieve the knowledge and experience learned in cases that are analogous to one or more current cases they are studying.
  • the fundamental functionality of the system is to represent drilling experiences in a highly structured fashion, so that the system can then be interrogated by querying for analogous cases. In this way, it supports certain decisions the engineer may make through the application of captured and stored organizational experience.
  • the system's preferred technology for implementing this is a logic-intensive computer system (implemented using a Description Logic called LOOM), which allows for the logical representative of concepts and relationships commonly conceptualized in the drilling domain. These are then organized into a subsumption hierarchy automatically by LOOM; such a set of defined concepts and relationship is called an ontology in the literature.
  • LOOM Description Logic
  • More complex concepts can be built/described using the more base/primitive concepts and relationships, such as a schema for the describing of bit run expectations elicited from field engineers including their decisions.
  • the system can also be used to constrain what is expressible by the field engineer in terms of the case's description, thereby limiting the bounds of his knowledge, and effectively putting extreme best practice limits, on, say, his drill bit selection in certain hardnesses of rocks.
  • the invention claimed is the utilization of a set of concepts and relationships to represent the drilling related knowledge and experience.
  • Figure 1 depicts one example of drilling operations conducted in accordance with the present invention.
  • a conventional rig 3 includes a derrick 5, derrick floor 7, draw works 9, hook 11, swivel 13, kelly joint 15, and rotary table 17.
  • a drillstring 19 which includes drill pipe section 21 and drill collar section 23 extends downward from rig 3 into wellbore 1.
  • Drill collar section 23 preferably includes a number of tubular drill collar members which connect together, including a measurement-while-drilling logging subassembly and cooperating mud pulse telemetry data transmission subassembly, which are collectively referred to hereinafter as "measurement and communication system 25".
  • drilling fluid is circulated from mud pit 27 through mud pump 29, through a desurger 31 , and through mud supply line 33 into swivel 13.
  • the drilling mud flows through the kelly joint and an axial central bore in the drillstring. Eventually, it exits through jets which are located in downhole drill bit 26 which is connected to the lowermost portion of measurement and communication system 25.
  • the drilling mud flows back up through the annular space between the outer surface of the drillstring and the inner surface of wellbore 1, to be circulated to the surface where it is returned to mud pit 27 through mud return line 35.
  • a shaker screen (which is not shown) separates formation cuttings from the drilling mud before it returns to mud pit 27.
  • measurement and communication system 25 utilizes a mud pulse telemetry technique to communicate data from a downhole location to the surface while drilling operations take place.
  • transducer 37 is provided in communication with mud supply line 33, This transducer generates electrical signals in response to drilling mud pressure variations. These electrical signals are transmitted by a surface conductor 39 to a surface electronic processing system 41 , which is preferably a data processing system with a central processing unit for executing program instructions, and for responding to user commands entered through either a keyboard or a graphical pointing device.
  • the mud pulse telemetry system is provided for communicating data to the surface concerning numerous downhole conditions sensed by well logging transducers or measurement systems that are ordinarily located within measurement and communication system 25.
  • Mud pulses that define the data propagated to the surface are produced by equipment which is located within measurement and communication system 25.
  • equipment typically comprises a pressure pulse generator operating under control of electronics contained in an instrument housing to allow drilling mud to vent through an orifice extending through the drill collar wall. Each time the pressure pulse generator causes such venting, a negative pressure pulse is transmitted which can be received by surface transducer 37.
  • An alternative conventional arrangement generates and transmits positive pressure pulses.
  • the circulating mud provides a source of energy for a turbine-driven generator subassembly which is located within measurement and communication system 25.
  • the turbine-driven generator generates electrical power for the pressure pulse generator and for various circuits including those circuits which form the operational components of the measurement-while-drilling tools.
  • batteries may be provided, particularly as a back-up for the turbine-driven generator.
  • FIG. 2 is a block diagram representative of the preferred embodiment of the present invention.
  • a drilling situation 101 is presented to user 103.
  • User 103 must make a decision concerning the drilling situation. The decision may include determining what types of downhole equipment to utilize, such as a selection of rock bits for particular types of drilling situations.
  • User 103 interacts through user interface 107 with database 111.
  • User interface includes a means for receiving a search request from user 103 and a means for providing an output to user 103 in a human-readable form.
  • the user generates a query which is defined by user-specified criteria 109 which is passed from user interface 107 to database 111.
  • Database 111 includes structured data which represents captured and stored organizational experience.
  • the structured data may include drilling knowledge 115 and/or drilling experience 117.
  • the user-specified criteria is utilized to query database 1111 in a manner which generates as an output the relevant or analogous knowledge and experience resident or present in database 111. This is passed through user interface 107 to user 103. User 103 then may utilize the knowledge to make drilling decision 105.
  • the user interface 107 and database 111 of Figure 2 are preferably constructed utilizing executable program instructions.
  • the program instructions are executed by a general purpose data processing system, such as that depicted in Figure 3.
  • data processing system 41 includes processor 12 which preferably includes a graphics processor, memory device and central processor (not shown). Coupled to processor 12 is video display 14 which may be implemented utilizing either a color or monochromatic monitor, in a manner well known in the art. Also coupled to processor 12 is keyboard 16. Keyboard 16 preferably comprises a standard computer keyboard which is coupled to the processor by means of cable 18.
  • mouse 20 is coupled to processor 12, in a manner well known in the art, via cable 22.
  • mouse 20 may include left button 24, and right button 26, each of which may be depressed, or "clicked", to provide command and control signals to data processing system 41.
  • any graphical pointing device such as a light pen or touch sensitive screen may be utilized to implement the method and apparatus of the present invention.
  • data processing system 41 may be implemented utilizing a so-called personal computer.
  • LOOM is a research project in the Artificial Intelligence Research Group at the University of Southern California's Information Sciences Institute. The goal of the project is to develop and field advanced tools for knowledge representation and reasoning in Artificial Intelligence.
  • LOOM software is the intellectual property of the University of Southern California.
  • the LOOM software is a language and environment for constructing intelligent applications.
  • LOOM is a knowledge representation system used to provide deductive support for a declarative knowledge portion of the LOOM language.
  • Declarative knowledge in LOOM consists of definitions, rules, facts, and default rules.
  • a deductive engine called a classifier utilizes forward-chaining, semantic unification, and object-oriented truth maintenance technologies to compile the declarative knowledge into a network designed to efficiently support on-line deductive query processing.
  • the LOOM system implements a logic-based pattern matcher for driving a production rule facility, and a pattern-directed method dispatching facility for supporting the definition of object-oriented methods.
  • LOOM has a high degree of integration between LOOM's declarative and procedural components. This permits programmers to utilize logic programming, production rule, and object- oriented programming paradigms in a single application.
  • the LOOM software can also be used as a deductive layer that overlays an ordinary CLOS network. In this mode, users can obtain many of the benefits of using LOOM without impacting the function or performance of their CLOS-based applications.
  • LOOM has been distributed to more than 80 universities and corporations, and is being used in numerous DARPA-sponsored projects in planning, software engineering, and intelligent integration of information. Licensing of LOOM:
  • the LOOM software is the intellectual property of the University of Southern California. It is not in the public domain; however, the University of Southern California grants permission to use this software for non-commercial purposes without a fee. If an application is covered by the terms of this fee-free license, it is not necessary to execute a written license agreement. More information about LOOM itself, its availability, and commercial licenses may be obtained from U.S.C. Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292-6695.
  • the LOOM software requires a Common Lisp compiler to function properly.
  • LOOM can be expected to function properly in an ANSI-compliant Common Lisp.
  • LOOM has been tested using the following Common Lisp compilers and platforms: Macintosh Common Lisp version 2.1-4.2; Franz Allegro Common Lisp;
  • the LOOM software is distributed as source files which must be compiled using a Common Lisp compiler. Several versions of LOOM are available. Versi ⁇ on 4.0 is the current release. The LOOM software is available in several formats via anonymous ftp from isi.edu in the pub/LOOM directory.
  • the LOOM 4.0 Source includes installation instructions, and release notes in at least the following files: Unix tar file (3.9 MB), Gzipped Unix tar file (866 kB), and Macintosh binhex file (1.4 MB).
  • the LOOM 3.0 Source includes installation instructions, and release notes in at least the following files: Unix tar file (3.5 MB), Gzipped Unix tar file (831 kB), and Macintosh binhex file (1.44 MB).
  • the LOOM 2.1 Source includes installation instructions, and release notes in at least the following files, Unix tar file (3.3 MB) and Gzipped Unix tar file (780 kB).
  • Loom reference material http://www.isi.edu/isd/LOOM/LOOM-HOME.html: Loom Users Guide (more structured description of language), Loom tutorial (introductory examples and basic concepts), Loom Reference Manual (reference manual of all functions, macros, constructs, grammar, etc.).
  • Ontosaurus From the Intelligent Systems Division of ISI/UCS, CA, USA.
  • CL-HTTP From MIT AI Laboratory, MIT, USA http://www.ai.mit.edu/ http://www.ai.mit.edu/projects/iiip/doc/cl-http/home-page.html Description Logics: These are general information sources on this technology. http://www.ida.liu.se/labs/iislab/people/patla/DL/index.html http://dl.kr.org/ http://www.cs.man.ac.uk ⁇ franconi/dl/course/
  • the conceptual part of the knowledge base is defined using concepts.
  • binary concepts otherwise known as roles or relations
  • unary concepts otherwise known as concepts or classes.
  • a introduction to these ideas in terms of Loom can be found in the Loom reference material.
  • the existential database part is maybe easier to edit. To this end a brief summary is given of what the new instances should look like when being entered.
  • the constructors most easily used in the database part of the knowledge base are "tell” and “about”. Tell is used to assert propositions and facts about the world or domain. About references the instance those propositions refer to.
  • CASE The instance below is a case instance. It has one formation sequence name and zero or more decisions and/or observations, (tell (:about Case-Name CASE
  • the instance below is that of a formation sequence instance. It was one or more formation names.
  • the instance below is a lithology. It has one or more hardness definitions.
  • the one below has a lithology hardness percent number meaning that there is number percent of the lithology with the hardness specified by the hardness instance in the ternary relation. Using the interval length from the formation this lithology is in, the lithology hardness amount in feet or meters can be calculated.
  • the present invention utilizes an ontological system which employs the LOOM modeling code which is available over the internet from the Information Sciences Institute.
  • LOOM 3.0 is the version that is currently available over the internet and can be found at www.isi.edu/isd/LOOM/LOOM-HOME.html. The following is a description of the ontological system, and includes representative code for modeling drilling information.
  • Top level concepts are those that are of type thing, which is a concept defined in Loom's theory BUILT-IN- THEORY. Any user-defined concept will be of type THING. (defconcept KNOWLEDGE_MANAGEMENT_CONCEPT)
  • Knowledge management concepts can be broken down into three broad categories including I CASES, II ENVIRONMENT, III DOWNHOLE EQUIPMENT. The following is a description of each of these three categories.
  • the types of information which is modeled in the CASES category includes date, location, decisions, issues, actions, goals, author, author's reasoning, company's reasoning, spin, observation, observation text, observation category, and observation formation.
  • the information which is structured in the environment category includes drilling fluid concepts, rock concepts, formation sequences, single formation sequences, multiple formation sequences, formations, single ontology formations, multiple ontology formations, lithologies, and hardnesses.
  • the types of information organized in the downhole equipment category includes bottomhole assemblies, bottomhole assembly components, and drill bits.
  • a case is (usually) a bit run case. That is, if some one experienced some bit run worthy of broadcast, then the formation sequence drilled should be represented, as well as the decisions taken on how to drill that formation sequence. That is, there are several decisions which must also be represented.
  • a decision can be a decision about the chosen drill bit, the mud, the BHA, the flow rate, and so on.
  • the case need not be an actual drill bit run if the person entering the case feels that he has some highly structured experience or knowledge he wishes to share.
  • a case also has a date on which it was captured and a location which it is supposed that the case is applicable to. Representative code for case identification follows, (defconcept CASE :is-primitive
  • Date The date is the date on which the bit run or generic knowledge was 'told' to the KB. It consists of at most 1 day, month, and/or year. Representative code follows. '
  • Location The location is the geographical position at which the bit run or knowledge is believed to be applicable to or came from. Representative code follows.
  • a decision here is presupposed to have several different dimensions. These include: issues, actions, goals, an author, a spin, and a reasoning. These dimensions, it is felt, provide a good balance between structure and text. The structure is to enable the formal representative and therefore search power required, and the textual for the more free flowing and readable information.
  • a parameter can be a drill bit, the flow rate, the BHA used, etc. Representative code follows.
  • Actions An action is the real-world consequent the engineer performed as part of his decision. Representative code follows.
  • Goals A goal is an objective which the engineer was aiming to achieve with that decision in particular. Representative code follows.
  • Author's Reasoning is a field of free-text for explanations for example of why a certain drill bit was chosen. This is to allow the possibility of incomplete, inaccurate, and even incoherent explanations for actions being stored; after all, the main reasoning or determinism for the action is the other structured information describing the circumstances in which the action was taken, such as the formation sequence. Representative code follows.
  • Company's Reasoning This is to allow a field which expresses the company's commonly agreed upon beliefs for the decision in question. Representative code follows.
  • the 'spin' is the type of the knowledge captured in terms of whether it is positive or negative in its effect. That is, if a case is entered which in effect is guiding the user to not using the action used in that example, then that should be considered a type of negative knowledge. If, on the other hand, a case has as its effect an engineer retrieving a case and executing the same or similar action, then that case should be considered positive. Representative code follows.
  • the environment here is described in terms of conceptual rock sequences. These sequences have some ontological constraints on them. For instance, if the user wishes to specify the depth and/or length of a particular section of lithology then that section has to be represented as a formation.
  • the super-structure larger than that is the formation sequence which can have one or more formations.
  • Each formation can have one or more lithology.
  • a lithology has no depth or length roles.
  • Drilling Fluid Concepts This is a concept representing the muds used in drilling oil wells. This is a top level concept in the ontology. Representative code follows.
  • Rock Concepts This is a top level concept representing all rock-related concepts, including lithologies, formations, and formation sequences.
  • a formation sequence is the constructor used to assemble all of the separate sub-sequences identified by the user when describing their well.
  • the formation sequence will usually be the section the drill bit drilled in its run.
  • a formation sequence has one or more constituent formations. Representative code follows.
  • a formation has one or more lithologies.
  • a formation is the conceptual modelling granularity at which the user should be representing any part of his well he feels should have represented interval lengths and depths. Even stringers, or production sands, if these, it is felt, need to have depths and/or lengths attached to their storage, would need to be modeled as 'formations'. If however, it is only stringers in a majority lithology, which have unimportant positions of depth in the over-all sequence due to uncertainty or irrelevance, then these stringers are 'lithologies' and the overall stringer and majority lithology sequence is then the 'formation'. Representative code follows.
  • a lithology is the basic rock type, e.g., sand, shale, etc.
  • Each lithology type also has an amount or a percentage amount for the formation in which it is present.
  • each lithology type is broken down into having a hardness, which then itself has an amount or a percentage amount for the lithology type.
  • complex conceptual descriptions can be built up of the well's statistical hardness profile. It can be envisaged that a tool useful in the construction of such conceptual description could be useful; as different levels of accuracy are obtainable, from the very coarse, to the very fine, grained and, obviously, the more fine grained, the more work is required to enter the definitions. Representative code follows.
  • Hardnesses There are various hardness concepts, each with their meaning specified by their UCS ranges. At the top level there is HARDNESS. Below that there are six second tier sub-categories of hardness starting at OkPSI, and increasing in steps of 5kPSI. There are also finer grained hardness sub-categories below each second tier category. These start at 0 kPSI and increase in steps of 2.5kPSI. Each of the six second tier hardness categories has a LOWER_ and an UPPER_ third tier sub-category. This gives twelve third-tier hardness categories.
  • harnesses An interesting note to be made about harnesses is the following. If there is known to 60% hard shale and 40% firm shale then that shale can be modeled with those stated percentages. If, however, it is only known that the shale has firm and hard harnesses in it, then a hardness FIRM_TO_HARD could be used as its hardness category (of which there is obviously 100% of). This modelling trick is useful when the exact percentage breakdown of the various constituent harnesses is unavailable. Representative code follows. (defconcept HARDNESS
  • a lithology may have one type (e.g., it is shale), they can have more than one hardness (e.g., that shale may have 100m of very soft rock with 300m soft rock also).
  • shale may have 100m of very soft rock with 300m soft rock also.
  • the following relation calculates the amount of lithology in feet of a certain hardness. It relates the amount, the hardness and the lithology in a ternary relation.
  • the following relation calculates the amount of lithology in a formation in feet.
  • the following relation calculates the amount of lithology in a formation in meters.
  • the following concept is an example of one of the second tier hardness concepts. It will have two subconcepts, upper soft and lower soft, beneath it. It is defined in terms of its ucs range. It is also a defined in that it is not primitive, and as such will recognize any hardness instance with its ucs range.
  • a down-hole tool is here defined to be anything that goes down hole and is not part of a recycling system (such as the fluid). So, a down-hole tool is a drill bit, a BHA component, and so on, Down-hole equipment concept is a top level concept. (defconcept DOWN-HOLE_EQUIPMENT_CONCEPT)
  • Bottom hole assemblies have at least 1 bha component. Representative code follows.
  • a BHA component is anything capable of being added to the BHA. So, motors, bits, MWD, VSS, under-reamers, etc, are all considered
  • Drill Bits A drill bit is a type of down-hole equipment concept with exactly 1 bit gauge. Representative code follows.
  • the functions retrieve and ask provide the interface to Loom's deductive query facility. Retrieve is used for retrieving facts (instances from a knowledge base), while ask is used to determine whether or not a proposition is true with respect to the currently stated rules and facts.
  • a query has one of the following forms
  • the ?m n are called output variables, and query is an open sentence in which the output variables appear unbound (unquantified). query can be any arbitrary expression in the first order predicate calculus (FOPC). The output variables must be prefixed with the character '?'.
  • Formation Sequences This is probably the most sophisticated form of querying that will be done of the knowledge base.
  • the two concepts likely to be of interest are formation sequences and formations (which are components of formation sequences). This is due to the more complex ternary relations used in the modelling of the lithologies (which are components of formations). It is also due to the ambiguity that will arise when users split up (or 'conceptualize') their wells into different formations (whilst, maybe, talking about essentially the same formation sequence) using different criteria. If that happens, then the user will want to look for formation sequences that, as a whole, contain, for example, 300 meters and over of very soft to soft shale type lithology.
  • the query below queries for cases where the drill bit (planning) action was to use a drill bit of type steel-tooth. This query will work if the drill-bit role has been filled with a concept. The second query will work if the role drill bit has been filled by an instance of a drill bit. For the querying of other actions, goals, and issues, the first type of querying would be used, as all their respective roles have their fillers in instances (of cases) represented as concepts. Representative code follows.

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Devices For Executing Special Programs (AREA)

Abstract

Procédé de forage d'un puits selon lequel des informations et données brutes de forage sont saisies et organisées selon une ontologie de concepts et relations définis, dans un format de base de données. Une interface permet à un opérateur d'interroger la base de données. Les interrogations reçues concernent des situations de forage spécifiques et un système de traitement de données est utilisé pour extraire de la base de données les connaissances ou le savoir-faire pertinents ou analogues concernant le forage. Les connaissances ou le savoir-faire pertinents ou analogues concernant le forage sont utilisés pour la prise de décisions en matière de forage.
PCT/US2000/016922 2000-06-20 2000-06-20 Systeme de gestion de connaissances en matiere de forage base sur des cas Ceased WO2001098631A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP00939969A EP1297244B1 (fr) 2000-06-20 2000-06-20 Systeme de gestion de connaissances en matiere de forage base sur des cas
PCT/US2000/016922 WO2001098631A1 (fr) 2000-06-20 2000-06-20 Systeme de gestion de connaissances en matiere de forage base sur des cas
AU2000254972A AU2000254972A1 (en) 2000-06-20 2000-06-20 Case-based drilling knowledge management system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2000/016922 WO2001098631A1 (fr) 2000-06-20 2000-06-20 Systeme de gestion de connaissances en matiere de forage base sur des cas

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002079601A1 (fr) * 2001-03-30 2002-10-10 Tracto-Technik Gmbh Dispositif et procede de determination de parametres de forage
WO2004031537A1 (fr) * 2002-09-30 2004-04-15 Schlumberger Canada Limited Extraction de donnees liees au mode de forage depuis un enregistrement de donnees de puits
US7092937B2 (en) 2003-04-07 2006-08-15 General Motors Corporation Vehicle diagnostic knowledge delivery
GB2460556A (en) * 2009-03-16 2009-12-09 Verdande Technology As Drilling operation monitoring using case based reasoning
WO2010010455A3 (fr) * 2008-07-23 2010-05-14 Schlumberger Technology B.V. Système et procédé d’automatisation de l'exploration ou de la production de ressources souterraines
US7931096B2 (en) 2005-08-30 2011-04-26 Sandvik Mining And Construction Oy Adaptive user interface for rock drilling rig
DE10123999B4 (de) * 2001-03-30 2011-08-11 Tracto-Technik GmbH, 57368 Vorrichtung und Verfahren zum Ermitteln von Bohrparametern
US8286726B2 (en) 2005-08-30 2012-10-16 Sandvik Mining And Construction Oy User interface for rock drilling rig
CN101781986B (zh) * 2010-02-09 2012-11-07 张超环 录井系统中曲线数据数值流传输与解析的方法与装置
WO2016018252A1 (fr) * 2014-07-29 2016-02-04 Halliburton Energy Services, Inc. Technique efficace de rapport de problemes associes a des operations de reservoir a une equipe de soutien
WO2017053491A1 (fr) * 2015-09-24 2017-03-30 Schlumberger Technology Corporation Système entrainé de modèle d'équipement de champ
WO2018111261A1 (fr) * 2016-12-14 2018-06-21 Landmark Graphics Corporation Classification automatique de rapports de forage avec traitement profond du langage naturel

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SA113340567B1 (ar) 2012-10-26 2015-07-07 بيكر هوغيس انكوربوريتد نظام وطريقة لمعالجة بيانات بئر باستخدام تحليل بيانات توبولوجية.

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997015749A2 (fr) * 1995-10-23 1997-05-01 Baker Hughes Incorporated Systeme de forage a boucle fermee

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997015749A2 (fr) * 1995-10-23 1997-05-01 Baker Hughes Incorporated Systeme de forage a boucle fermee

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ARTIFICIAL INTELLIGENCE RESEARCH GROUP AT THE UNIVERSITY OF SOUTHERN CALIFORNIA'S INFORMATION SCIENCES INSTITUTE.: "Loom Project Home Page", INTERNET ARTICLE, XP002145728, Retrieved from the Internet <URL:http://www.isi.edu/isd/LOOM/> [retrieved on 20000825] *
D.A. CURRY ET AL: ""Drilling Performance Guidelines" - A Tool for Sharing Drilling-Related Knowledge and Experience", SPE/IADC # 52804, 9 March 1999 (1999-03-09), pages 1 - 10, XP000912026 *
INGE NORDBO: "Drilling Expert Systems", 3RD NORWEGIAN PETROLEUM SOCIETY NORTHERN EUROPEAN DRILLING CONFERENCE, 2 November 1987 (1987-11-02), XP000912259 *
MARTINO CORTI ET AL: "Real-Time Expert System for Drilling Operations Support", SPE # 24272, 25 May 1992 (1992-05-25), pages 117 - 126, XP002145820 *
R.G. FENOUL ET AL: "Use of New Technologies for Knowledge-based Systems Development", SPE # 24418, 19 July 1992 (1992-07-19), pages 21 - 28, XP002145825 *

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002079601A1 (fr) * 2001-03-30 2002-10-10 Tracto-Technik Gmbh Dispositif et procede de determination de parametres de forage
DE10123999B4 (de) * 2001-03-30 2011-08-11 Tracto-Technik GmbH, 57368 Vorrichtung und Verfahren zum Ermitteln von Bohrparametern
WO2004031537A1 (fr) * 2002-09-30 2004-04-15 Schlumberger Canada Limited Extraction de donnees liees au mode de forage depuis un enregistrement de donnees de puits
US6782322B2 (en) 2002-09-30 2004-08-24 Schlumberger Technology Corporation Method, apparatus and computer program product for creating ream section from memory data based on real-time reaming
GB2409313A (en) * 2002-09-30 2005-06-22 Schlumberger Holdings Extracting drilling mode related data from well data logging
US7092937B2 (en) 2003-04-07 2006-08-15 General Motors Corporation Vehicle diagnostic knowledge delivery
US8286726B2 (en) 2005-08-30 2012-10-16 Sandvik Mining And Construction Oy User interface for rock drilling rig
US7931096B2 (en) 2005-08-30 2011-04-26 Sandvik Mining And Construction Oy Adaptive user interface for rock drilling rig
WO2010010455A3 (fr) * 2008-07-23 2010-05-14 Schlumberger Technology B.V. Système et procédé d’automatisation de l'exploration ou de la production de ressources souterraines
US8606734B2 (en) 2008-07-23 2013-12-10 Schlumberger Technology Corporation System and method for automating exploration or production of subterranean resource
GB2460556A (en) * 2009-03-16 2009-12-09 Verdande Technology As Drilling operation monitoring using case based reasoning
CN102395754A (zh) * 2009-03-16 2012-03-28 沃丹德科技股份公司 一种用于监控钻井操作的方法和系统
CN102395754B (zh) * 2009-03-16 2014-12-10 沃丹德科技股份公司 一种用于监控钻井操作的方法和系统
CN101781986B (zh) * 2010-02-09 2012-11-07 张超环 录井系统中曲线数据数值流传输与解析的方法与装置
WO2016018252A1 (fr) * 2014-07-29 2016-02-04 Halliburton Energy Services, Inc. Technique efficace de rapport de problemes associes a des operations de reservoir a une equipe de soutien
GB2540502A (en) * 2014-07-29 2017-01-18 Halliburton Energy Services Inc Efficient way of reporting issues associated with reservoir operations to support team
US10370939B2 (en) 2014-07-29 2019-08-06 Halliburton Energy Services, Inc. Efficient way of reporting issues associated with reservoir operations to support team
GB2540502B (en) * 2014-07-29 2021-02-24 Halliburton Energy Services Inc Efficient way of reporting issues associated with reservoir operations to support team
WO2017053491A1 (fr) * 2015-09-24 2017-03-30 Schlumberger Technology Corporation Système entrainé de modèle d'équipement de champ
GB2559895A (en) * 2015-09-24 2018-08-22 Geoquest Systems Bv Field equipment model driven system
WO2018111261A1 (fr) * 2016-12-14 2018-06-21 Landmark Graphics Corporation Classification automatique de rapports de forage avec traitement profond du langage naturel
GB2571645A (en) * 2016-12-14 2019-09-04 Landmark Graphics Corp Automatic classification of drilling reports with deep natural language processing

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