Neural Network Model for Permeability Prediction from Reservoir Well Logs

Abstract

The estimation of the formation permeability is considered a vital process in assessing reservoir deliverability. The prediction of such a rock property with the use of the minimum number of inputs is mandatory. In general, porosity and permeability are independent rock petrophysical properties. Despite these observations, theoretical relationships have been proposed, such as that by the Kozeny–Carmen theory. This theory, however, treats a highly complex porous medium in a very simple manner. Hence, this study proposes a comprehensive ANN model based on the back propagation learning algorithm using the FORTRAN language to predict the formation permeability from available well logs. The proposed ANN model uses a weight visualization curve technique to optimize the number of hidden neurons and layers. Approximately 500 core data points were collected to generate the model. These data, including gamma ray, sonic travel time, and bulk density, were collected from numerous wells drilled in the Western Desert and Gulf areas of Egypt. The results show that in order to predict the permeability accurately, the data set must be divided into 60% for training, 20% for testing, and 20% for validation with 25 neurons. The results yielded a correlation coefficient (R2) of 98% for the training and 96.5% for the testing, with an average absolute percent relative error (AAPRE) of 2.4%. To validate the ANN model, two published correlations (i.e., the dual water and Timur’s models) for calculating permeability were used to achieve the target. In addition, the results show that the ANN model had the lowest mean square error (MSE) of 0.035 and AAPRE of 0.024, while the dual water model yielded the highest MSE of 0.84 and APPRE of 0.645 compared to the core data. These results indicate that the proposed ANN model is robust and has strong capability of predicting the rock permeability using the minimum number of wireline log data.

Penerapan Model Random Forest Untuk Prediksi Penyebaran Porositas pada Lapangan X

“…Dua di antara kararakteristik yang terdapat pada reservoir adalah porositas dan permeabilitas yang merupakan karakteristik utama dalam proses karakterisasi reservoir. Interpretasi well logging banyak diterapkan untuk dapat mengestimasi nilai porositas dan permeabilitas pada kedalaman yang bervariasi dikarenakan biaya yang minimal dibandingkan proses sample core (Helle & Bhatt, 2002;Lim & Kim, 2004, sebagaimana dikutip dalam Abdel Azim & Aljehani, 2022). Sebaliknya, proses sample core merupakan sumber informasi yang terpercaya dikarenakan sample core dapat langsung diambil dari sumur bor.…”

Reservoir parameters prediction based on spatially transferred long short-term memory network

“…A hybrid data-driven model consisting of a PSO, SVR, and deep learning network was built by Gu et al [ 9 ] to produce more accurate predictions. Reda Abdel Azim et al [ 10 ] propose an artificial neural network (ANN) model based on the back propagation learning algorithm to predict formation permeability from well logs, using a weight visualization curve technique to optimize the number of hidden neurons and layers. In terms of model design, structural modification, parameter setting, and data pre-processing, existing approaches for the prediction of reservoir parameters tend to deliver better results.…”

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson’s ratio of selected rock types

“…In addition, several attempts have been made to develop predictive relationships for different physic-mechanical parameters of rocks such as compressive strength, tensile strength, shear strength, elastic moduli, porosity, permeability, acoustic wave velocities, etc. 16 , 29 – 32 . There is limited literature available that focuses on the predictive modeling of dynamic Poisson’s ratio.…”