Creation of software tool for long-term investment planning with a view to the effective development of oil fields

Насыбуллин, А В; Sattarov, Ram.Z.; Latifullin, Farit M.; Денисов, О В; Chirikin, A. V.

doi:10.24887/0028-2448-2019-12-128-131

Cited by 2 publications

(3 citation statements)

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“…For example, the work [7] proposes the methods for solving the problem of developing recommendations on the GTM appointment. The practical use of the approach made it possible to form a knowledge base that includes many rules of the following type: IF the horizon type is "Devon" AND the period Rzab<Rnas is more than 0.5 years AND the water cut is CLOSE ([0.70]%) AND the productivity percentage drop is ABOUT ([17.100]%) AND the reservoir pressure is ABOUT ([12.60] MPa) AND the oil production rate is ABOUT ([2,maxOil production rate] t/day) AND the type of well is "Extractive" AND the reservoir injectivity is MEDIUM or GREAT, THEN it is possible to use the DISIN + HCl technology at the well.…”

Section: Methodsmentioning

confidence: 99%

“…Currently, artificial intelligence technologies are being actively introduced to solve practical problems in various subject areas of human activity [1][2][3][4][5]. One of these areas is the oil industry, for which it is important to solve a number of problems to increase the oil field development efficiency [6][7][8]. The task of selecting geological and technical measures (GTM) to enhance oil recovery is urgent in this subject area [9].…”

Section: Introductionmentioning

confidence: 99%

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Neurofuzzy Model of Formation of Knowledge Bases for Selection of Geological and Technical Measures in Oil Fields

Panischev¹,

Ahmedshina²,

Kataseva³

et al. 2020

International Journal of Engineering Research and Technology

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This paper poses and solves the problem of developing the upto-date neuro-fuzzy model of formation of a knowledge base for an intelligent decision-making support system for selection of geological and technical measures in oil fields. The analysis of the traditional approach to the formation of fuzzy knowledge bases made it possible to reveal its shortcomings associated with the need to attract experts, structure and formalize the system of decision-making rules by them. This process is laborious and does not always provide an acceptable result. To eliminate the disadvantages of the traditional approach, we proposed an approach to the automatic formation of a knowledge base based on the construction of a neuro-fuzzy model of a collective of fuzzy neural networks. We formulated the requirements in view of the formed fuzzy rules. We developed a scheme for using the rules of the knowledge base to solve the problem of selecting geological and technical measures in oil fields. We tested the generated knowledge base on the example of solving the problem of selecting geological and technical measures for various wells of the Feofanovskoye Field. Application of the knowledge base made it possible to select a list of optimal measures for given wells. The experiment results are satisfactory and are confirmed by the positive expert assessments, selecting geological and technical measures at this field.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neurofuzzy Model of Formation of Knowledge Bases for Selection of Geological and Technical Measures in Oil Fields

Panischev¹,

Ahmedshina²,

Kataseva³

et al. 2020

International Journal of Engineering Research and Technology

View full text Add to dashboard Cite

show abstract

“…At present, the problem of oil field development efficiency increase [1][2][3], especially those at the late stage of their development [4], is becoming increasingly important. This stage is characterized by the presence of a large share of hard-to-recover oil reserves, and its extraction rate decrease [5,6].…”

Section: Introductionmentioning

confidence: 99%