Automated Geosteering Optimization Using Machine Learning

Abstract Geosteering is an important area and its quality determines the efficiency of formation drilling by horizontal wells, which directly affects the project NPV. This paper presents the automated geosteering optimization platform which is based on live well data. The platform implements online corrections of the geological model and forecasts well performance from the target reservoir. The system prepares recommendations of the best reservoir production interval and the direction for horizontal well placements based on reservoir performance analytics. This paper describes the stages of developing a comprehensive system using machine-learning methods, which allows multivariate calculations to refine and predict the geological model. Based on the calculations, a search for the optimal location of a horizontal well to maximize production is carried out. The approach realized in the work takes into account many factors (some specific features of geological structure, history of field development, wells interference, etc.) and can offer optimum horizontal well placement options without performing full-scale or sector hydrodynamic simulation. Machine learning methods (based on decision trees and neural networks) and target function optimization methods are used for geological model refinement and forecasting as well as for selection of optimum interval of well placement. As the result of researches we have developed the complex system including modules of data verification and preprocessing, automatic inter-well correlation, optimization and target interval selection. The system was tested while drilling hydrocarbons in the Western Siberian fields, where the developed approach showed efficiency.

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Application of Machine Learning Algorithms for Solving Problems in the Oil and Gas Sector

Intellekt Sist Proizv ◽

10.22213/2410-9304-2021-3-55-64 ◽

2021 ◽

Vol 19 (3) ◽

pp. 55-64

Author(s):

K. N. Maiorov ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Reinforcement Learning ◽

Machine Learning Algorithms ◽

Design Stage ◽

Learning Methods ◽

Field Development ◽

Advantages And Disadvantages ◽

Machine Learning Methods ◽

The Neural Network

The paper examines the life cycle of field development, analyzes the processes of the field development design stage for the application of machine learning methods. For each process, relevant problems are highlighted, existing solutions based on machine learning methods, ideas and problems are proposed that could be effectively solved by machine learning methods. For the main part of the processes, examples of solutions are briefly described; the advantages and disadvantages of the approaches are identified. The most common solution method is feed-forward neural networks. Subject to preliminary normalization of the input data, this is the most versatile algorithm for regression and classification problems. However, in the problem of selecting wells for hydraulic fracturing, a whole ensemble of machine learning models was used, where, in addition to a neural network, there was a random forest, gradient boosting and linear regression. For the problem of optimizing the placement of a grid of oil wells, the disadvantages of existing solutions based on a neural network and a simple reinforcement learning approach based on Markov decision-making process are identified. A deep reinforcement learning algorithm called Alpha Zero is proposed, which has previously shown significant results in the role of artificial intelligence for games. This algorithm is a decision tree search that directs the neural network: only those branches that have received the best estimates from the neural network are considered more thoroughly. The paper highlights the similarities between the tasks for which Alpha Zero was previously used, and the task of optimizing the placement of a grid of oil producing wells. Conclusions are made about the possibility of using and modifying the algorithm of the optimization problem being solved. Аn approach is proposed to take into account symmetric states in a Monte Carlo tree to reduce the number of required simulations.

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Field Development Optimization Using Machine Learning Methods to Identify the Optimal Water Flooding Regime

10.2118/206533-ms ◽

2021 ◽

Author(s):

Alexey Vasilievich Timonov ◽

Arturas Rimo Shabonas ◽

Sergey Alexandrovich Schmidt

Keyword(s):

Machine Learning ◽

Optimization Algorithms ◽

Machine Learning Algorithms ◽

Water Flooding ◽

Learning Models ◽

Learning Methods ◽

Injection Wells ◽

Field Development ◽

Machine Learning Methods ◽

Machine Learning Models

Abstract The main technology used to optimize field development is hydrodynamic modeling, which is very costly in terms of computing resources and expert time to configure the model. And in the case of brownfields, the complexity increases exponentially. The paper describes the stages of developing a hybrid geological-physical-mathematical proxy model using machine learning methods, which allows performing multivariate calculations and predicting production including various injection well operating regimes. Based on the calculations, we search for the optimal ratio of injection volume distribution to injection wells under given infrastructural constraints. The approach implemented in this work takes into account many factors (some features of the geological structure, history of field development, mutual influence of wells, etc.) and can offer optimal options for distribution of injection volumes of injection wells without performing full-scale or sector hydrodynamic simulation. To predict production, we use machine learning methods (based on decision trees and neural networks) and methods for optimizing the target functions. As a result of this research, a unified algorithm for data verification and preprocessing has been developed for feature extraction tasks and the use of deep machine learning models as input data. Various machine learning algorithms were tested and it was determined that the highest prediction accuracy is achieved by building machine learning models based on Temporal Convolutional Networks (TCN) and gradient boosting. Developed and tested an algorithm for finding the optimal allocation of injection volumes, taking into account the existing infrastructure constraints. Different optimization algorithms are tested. It is determined that the choice and setting of boundary conditions is critical for optimization algorithms in this problem. An integrated approach was tested on terrigenous formations of the West Siberian field, where the developed algorithm showed effectiveness.

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