Prediction of the pore structure by machine learning techniques in the carbonate reservoirs in Iraq H oilfield

Abstract

Pore structure impacts the capability of seepage pattern of subsurface fluid mineral and mineral exploitation efficiency. Because of the strong heterogeneity in carbonate reservoirs, the pore structure is nonlinearly varying and complex in reservoirs. It is necessary to establish a method for pore structure type (PST) prediction. Machine learning provides an efficient prediction method by finding the relationship between core test data and well‐logging data. In this paper, a reservoir identification method based on Gradient Boost Decision Trees (GBDT) is proposed for the pore structure characteristics of carbonate reservoirs integrating core test and logging data. Core testing data are utilized to form the training set for PST prediction. Then, we adopt the mutual information method to optimize the logging data as the input of the machine learning model. GBDT hyperparameters are optimized using the learning curve to make it have optimal performance. Finally, we compare the predicted result of GBDT with K‐Nearest Neighbor and support vector machines, demonstrating the superior performance of GBDT. The results indicate that the four PS are different in some properties, such as permeability, pore radius and shape of pore and throat. 20%, 10 and 0.15 are recommended as the optimal parameter values for Maximum feature, iteration number and Sub‐sample number to improve GBDT accuracy in carbonate reservoir PST prediction. Evaluation indicators of machine learning, including the Kappa coefficient, Heming distance, Jaccard similarity coefficient and Confusion matrices, indicate that GBDT has better performance. This research proves the high accuracy and applicability of machine learning technology in the carbonate reservoir and also provides a meaningful reference for the identification of the carbonate reservoirs in the central area of Iraq.

Application of artificial intelligence in geotechnical and geohazard investigations

“…Machine learning presents a promising solution by establishing a correlation between core test data and well logging data for predicting PS characteristics. Lu et al (2023) propose a Gradient Boost Tree (GBDT)-based reservoir identification method for predicting PS characteristics in carbonate reservoirs by integrating core test and logging data. They utilize core testing data to generate a training set for PS prediction and employ the mutual information method to optimize logging data as the input for the machine learning model.…”

Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network

“…In order to verify the effect of the DNN model more comprehensively, we selected three mature machine learning algorithms, i.e., Support Vector Regression (SVR), Random Forest Regression (RFR) and XGBoost Regression (XGBR), which have been proved to be effective in petrophysical characterization and microscopic heterogeneity [51][52][53], to synchronously predict the pore-throat radius. Support Vector Regression, which is derived from Support Vector Machine, is used to find a hyper-plane that has the smallest margin of ε deviation from the actually obtained targets [71]; meanwhile, the ε tube needs to involve the maximum number of training data.…”

“…Currently, in regard to petrophysical characterization and microscopic heterogeneity, many machine learning methods have been devoted to generating more accurate predictions. Lu et al [51] predicted the pore structure type of the carbonate reservoirs in the Iraq H oilfield by using Gradient Boost Decision Trees and Support Vector Regression. In the procedure of analyzing the permeability contribution of multiscale pore structure, Wang and Sun [52] classified a multiscale pore structure into different rock types using the Random Forest algorithm.…”