Automatic Well Log Analysis Across Priobskoe Field Using Machine Learning Methods

Abstract

This paper is devoted to the testing of of automatic well logs interpretation testing, developed on the basis of machine learning methods. The basis of the method presented in the paper is recurrent artificial neural networks. For their training and adjustment, log curves and their corresponding interpretation of different years are used. The set of well data is divided into training, validation and test samples. The resulting tool is set up on training and validation samples, and then used on a test sample of wells for which the interpretatoin was hidden, in order to automatically predict net pays and compare the results with the interpretation performed by an expert petrophysicist. For the test sample, traditional machine learning metrics metrics and special geological were calculated to assess the quality of the algorithm. During the work a number of experiments were carried out, in which the dependence of the forecast quality was estimated not only on the different architecture and settings of the artificial neural network, but also on the amount of input data. The iterative approach in the research allowed to determine the best parameters for the solution of the task. For each well of the test set, a forecast of reservoir intervals distribution is made. The resulting interpretation shows high accuracy, both in terms of different mathematical metrics, and the results of analysis and evaluation of the expert petrophysics. Also, during the experiments, an important conclusion was made about the generalizing ability of the proposed methodology. The use of several variants of interpretation of well log data, performed by different specialists at different times and on the basis of different petrophysical models, allows to generalize and use all the accumulated experience of well logs interpretation, thereby improving the quality of the forecast. The main conclusion of the study can be considered a statement about the efficient applicability of machine learning algorithms for automatic well logs interpretation.

Tight and Shale Oil Exploration: A Review of the Global Experience and a Case of West Siberia

“…The application of data-driven approaches for the study of West Siberian shales was dedicated mostly to the characterization of geological and petrophysical properties of tight oil reservoirs. With respect to improved and enhanced oil recovery methods, most of the research was dedicated to optimization of hydraulic fracturing techniques [145][146][147][148]. A summary of the pore-scale modeling techniques is presented in Table 5.…”

A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

“…Onalo (2018) et al [14] used neural networks to obtain information from open-hole logging data and reconstruct open-hole acoustic logging data. Belozerov (2018) et al [7] used neural networks to identify reservoir locations from logging data, and Gkortsas (2019) et al [15] used support vector machines and neural networks to automatically identify ultrasonic waveform characteristics, which can predict additional information about the longitudinal wave velocity of annular materials in cased wells. Deepak Kumar Voleti et al [9] (2020) established different machine learning algorithms, such as random forest and neural network prediction based on CBL-VDL, and ultrasonic imaging data, to output the prediction results of cementing quality.…”

“…At the same time, further oil and gas well development may depend on the evaluation results of cementing quality. Therefore, cementing interpretation is performed under time pressure [7]. Thus, the evaluation method urgently needs to be improved.…”