Fuzzy Multi-Criterial Choice of Geological and Technical Measures

The work solves the problem of automating the process planning of assigning geological and technical measures (GTM) at oil fields in conditions of uncertainty. A decision support system is being developed to help an expert make an informed decision about the method of influence of geological and technical measures on an oil reservoir. From the point of view of the imposed restrictions on the choice of geological and technical measures, various types of geological and physical parameters are highlighted. To solve this problem, a fuzzy-production model is proposed for the representation of expert knowledge. A feature of this model is the possibility of different types of parameters use to impose restrictions on the choice of geological and technical measures, using fuzzy restrictions and setting their weights, as well as formalizing the degree of an expert confidence in the reliability of the rule being formed. They provided the possibility of fuzzy modifier use in the conditions of fuzzy production rules for fuzzy constraint correction. To determine the weights of fuzzy constraints in the conditions of the rules, an approach is used based on a multi-criteria assessment of constraints, carried out using the hierarchy analysis method (HAM). The following were used as the criteria for evaluation the weights: the importance of the corresponding geological and physical parameter for an expert, the completeness of the available information on the studied parameter, the relevance of the values, the complexity of obtaining the values. The final choice of geological and technical measures is carried out on the basis of a fuzzy multi-criteria choice according to the following criteria: satisfaction of the fuzzy production model limitations, high technological efficiency, high economic effect, and the impact on the environment. Based on the knowledge of experts, a knowledge base has been formed that includes fuzzy production rules for choosing 81 different geological and technical measures at production wells using the restrictions on 15 geological and physical parameters. The knowledge base has been tested at the wells of the Feofanovskoye field, Alkeevskaya, Chishminskaya areas. The development of recommendations was carried out in conditions of information incompleteness on a number of parameters of the set. The results generated by the decision support system correspond to the decisions made by the experts. KeywordsKnowledge Base, Fuzzy Logic, Fuzzy Production Model, Geological And Technical Event, Hierarchy Analysis Method, Decision Support

[1]  Oleksii Shushura,et al.  Construction of Membership Functions in Fuzzy Modeling Tasks using the Analytic Hierarchy Process , 2020, International Journal of Advanced Trends in Computer Science and Engineering.

[2]  A S Fatin Amirah,et al.  Generating project risk membership functions based on experts’ estimates and alpha-cut variations , 2020, Journal of Physics: Conference Series.

[3]  Mohammad Ali Baghapour,et al.  Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System , 2020, Water Resources Management.

[4]  Rf Almetyevsk Tatneft Pjsc,et al.  Creation of software tool for long-term investment planning with a view to the effective development of oil fields , 2019, Neftyanoe khozyaystvo - Oil Industry.

[5]  D. V. Kataseva,et al.  Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing , 2019 .

[6]  Y. S. Buana,et al.  Paper Production Cost Analysis Of Improved Oil Recovery Projects Based On Field Development Plan In Indonesia , 2019, SPE Kuwait Oil & Gas Show and Conference.

[7]  Alisa Makhmutova,et al.  Big spatio‐temporal data mining for emergency management information systems , 2019, IET Intelligent Transport Systems.

[8]  T. A. Kireeva.  Cationic exchange between injected water and rock as a scaling factor in oil fields development , 2019, Neftânoe hozâjstvo.

[9]  Alexey S. Katasev,et al.  Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty , 2019, Computer Research and Modeling.

[10]  I. Plotnikova,et al.  Modeling the development of oil fields, considering the mature fields reforming and refill by the deep hydrocarbons , 2019, Neftânoe hozâjstvo.

[11]  A. Yudin,et al.  Improving Economics of Hard-to-Recover Reserves Development. Case Study of Achimov Formation at Prirazlomnoe Oil Field. , 2018, Day 3 Wed, October 17, 2018.

[12]  G. Caumon.  Geological Objects and Physical Parameter Fields in the Subsurface: A Review , 2018 .

[13]  D. V. Tomashev,et al.  The Influence of Planetary Geodynamics on the Success of Geological and Technical Measures for the Development of Oil and Gas Fields , 2018, GeoBaikal 2018.

[14]  P. Zimin,et al.  Development of an automated matching algorithms geological and technical measures and criteria Well-ranking candidates on the basis of fuzzy sets (Russian) , 2016 .

[15]  I. Anikin,et al.  Fuzzy control based on new type of Takagi-Sugeno fuzzy inference system , 2015, 2015 International Siberian Conference on Control and Communications (SIBCON).

[16]  lgor Anikin,et al.  New Type of Takagi-Sugeno Fuzzy Inference System as Universal Approximator , 2014 .

[17]  Sung Ho Ha,et al.  Applying knowledge engineering techniques to customer analysis in the service industry , 2007, Adv. Eng. Informatics.

[18]  Sofiane Achiche,et al.  Fuzzy decision support system knowledge base generation using a genetic algorithm , 2001, Int. J. Approx. Reason..

[19]  Neelu Khare,et al.  Introduction to Concept Modifiers in Fuzzy Soft Sets for Efficient Query Processing , 2020 .

[20]  Alexey Kirillov,et al.  Prototype of Classifier for the Decision Support System of Legal Documents , 2019, SSI.

[21]  I. Plotnikova,et al.  Consideration of the processes of oil deposit reformation during long-term operation and deep feeding in modeling the development of oil fields , 2018 .

[22]  Rf Almetyevsk Tatneft Pjsc,et al.  Analyzing effectiveness of the terrigenous reservoirs hydrofracturing at South-Romashkinskaya area of Romashkinskoe oil field at the late stage of development , 2018 .

[23]  R. Khisamov,et al.  Improvement of the Development Efficiency of Reserves Difficult to Recover Using Horizontal and Multibranch Wells on the Example of Nekrasovsky Field Developed by Carbon-Oil LLC , 2017 .

[24]  A. S. Katasev,et al.  Expert diagnostic system of water pipes gusts in reservoir pressure maintenance processes , 2016, 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).

[25]  Irina Perfilieva,et al.  Web-Based System for Enterprise Performance Analysis on the Basis of Time Series Data Mining , 2016 .

[26]  Bernadette Bouchon-Meunier,et al.  Fuzzy Modifiers at the Core of Interpretable Fuzzy Systems , 2015, Fifty Years of Fuzzy Logic and its Applications.

[27]  Yau-Hwang Kuo,et al.  The Reduction of Interval Type-2 LR Fuzzy Sets , 2014, IEEE Transactions on Fuzzy Systems.

[28]  Elena Irina Neaga,et al.  Semantics Enhancing Knowledge Discovery and Ontology Engineering Using Mining Techniques: A Crossover Review , 2007 .