Decision Support System for Tight Oil Fields Development Achimov Deposits and Their Analogues Using Machine Learning Algorithms

Abstract

The aim of this work is to develop an approach to multivariate optimization of development systems for tight oil reservoirs of the Achimov formation, where large volumes of drilling of RN-Yuganskneftegaz LLC are currently concentrated on. The approach described in the paper is an integral part of the corporate module "Decision Support System for drilling out new sections of tight oil reservoirs", which allows making quick design decisions for new drilling sites of target objects. This work discusses the main parts of the integrated solution of this system that will be embedded into corporate software. Also, the description of the global approach and obtained results are presented. The main idea of this project is based on automatic assignment of the prospective development zone to an existing cluster-analog, based on well logs response in exploration wells. Following this interpretation, the potential performance of various development systems is evaluated and the optimal one is selected. Within the framework of these projects the following tasks were solved: Wells clustering in Achimov deposits and their analogs. The geological heterogeneity and reservoir connectivity were characterized and a special algorithm for wells assignments to an existing cluster was developed, that is done by: Wells clustering depending on their petrophysical properties derived from well logs interpretation via k-means algorithm. Wells classification with a use of neural network. Multivariate 3D dynamic modeling and creation of surrogate models to provide predictions of reservoir simulation results. Development of the software package with all mentioned functionality being implemented.