Nowadays, due to continuous depletion of conventional reserves, oil producers seek alternatives for resource base maintenance in unconventional hard-to-recover deposits. In particular, Rosneft Oil Company every year increase the number of new wells drilled in low-permeable ((0.1-0.5) 10-3 mkm2) Achimov formation reservoirs with high vertical heterogeneity, weak connectivity between sand bodies and net-to-gross ratio between 10 and 30%.Currently, a large number of studies are being carried out in the design of optimal systems for the development of low-permeability and low-connectivity reservoirs and methods for creating physically meaningful models for a technical and economic assessment of the effectiveness of design solutions. However, few of them describe systematic approaches to the selection of optimal development systems and, in particular, to making decisions about adjusting the development mode.
The objective of this work is to provide a description of the Automated Intelligent Assistant, “Decision Support System for Tight Oil Fields Development”, that enables an automatic selection of the optimal wells placement patterns for prospective drilling areas in unconventional reservoirs. The article describes the main parts of the system integrated into a complex module Smart-GIR in Rosneft Oil Company corporate software package RN-KIN. The proposed solution engages machine learning algorithms in its workflow. The project’s scope includes the development of the algorithms for reservoirs cauterization in Achimov deposits and their analogs and creation the database that comprises the interpreted output of multivariate reservoir simulation and developed a neural network for replication of numerical calculations.
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