A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning

Abstract

In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density–sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast the pore pressure in well X-1. Upon comparison with real pore pressure data, the prediction error is found to be under 5%, thus establishing it as a representative measure of the reservoir’s pore pressure. Intelligent prediction models for pore pressure were developed using the KNN, Extra Trees, Random Forest, and LightGBM algorithms. The models utilized five categories of well logging data, sonic time difference (DT), gamma ray (GR), density (ZDEN), neutron porosity (CNCF), and well diameter (CAL), as input. After training and comparison, the results demonstrate that the LightGBM model exhibits significantly superior performance compared to the other models. Specifically, it achieves R2 values of 0.935 and 0.647 on the training and test sets, respectively. The LightGBM model is employed to predict the pore pressure of two wells neighboring well X-1. Subsequently, the predicted data are juxtaposed with the actual pore pressure measurements to conduct error analysis. The achieved prediction accuracy exceeds 90%. This study delivers a comprehensive analysis of pore pressure prediction within sections exhibiting anomalously high pressure, consequently furnishing scientific insights to facilitate both secure and efficient drilling operations within the X block.

Machine Learning in Reservoir Engineering: A Review

“…However, obtaining this parameter experimentally is time-consuming and expensive. A machine learning model is constructed by using the Random Forest (RF), Decision Tree (DT) and K Nearest Neighbor (KNN) methods, taking into account experimental conditions such as injection rate, overburden pressure, and fracturing fluid viscosity, as well as some of the key features needed to calculate the breakdown pressure of the rock [8,9]. After optimizing the model parameters using the grid search optimization method, the breakdown pressure prediction accuracy of unconventional formations is 95% [10].…”