A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

Abstract

Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

A Physics-Informed Neural Network Approach for Surrogating a Numerical Simulation of Fractured Horizontal Well Production Prediction

“…Gaussian process regression (GPR), convolutional neural networks (CNNs), and support vector machines (SVMs) have been used as proxy models for numerical simulation models of fractured horizontal wells with parallel, equal-length hydraulic fractures. These proxy models have been used to predict gas production and to obtain optimal fracture half-lengths and horizontal well lengths (Wang et al, 2021) [1]. Multi-layer perceptron (MLP) can be used to construct surrogate models to predict the production of gas wells with parallel, equal-length hydraulic fractures on a two-dimensional plane (Wang et al, 2021) [2].…”

“…However, the input variables of the proxy models mentioned in these studies are often oversimplified. For example, among the previously mentioned proxy models, CNN, GPR, and SVM (Wang et al, 2021) [1] require that the length of the hydraulically fractured fractures in the dataset take only a fixed number of four values; the training set of the MLP network (Wang et al, 2021) [2] contains only four variables, and these four features can only take a fixed number of four values; the tree-based ensemble model (Xue et al, 2019) [3] requires that the dataset can only have these four variables, which can only take a fixed number of three values; and the input data of the transformer (Wang et al [5]) need to be preprocessed by PCA (principal component analysis) to decrease its dimension, but the variables generated by PCA often pose challenges in terms of interpretation. Although reducing the dimensions of the input data or simplifying the input data can reduce the complexity of machine learning models and make training easier, this also decreases the performance of the proxy models.…”

Prediction of Single-Well Production Rate after Hydraulic Fracturing in Unconventional Gas Reservoirs Based on Ensemble Learning Model

“…We selected these algorithms due to their proven effectiveness in handling high-dimensional data and capturing the complex non-linear relationships inherent in reservoir and production data. These models are particularly adept at dealing with the imbalanced and noisy nature of the datasets typically encountered in gas production prediction tasks [29][30][31][32]. By integrating geological, reservoir, engineering parameters, and production data, several basic models for predicting the production in unconventional gas single wells was established, and the accuracy of each model was validated.…”

Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical Application

“…The development of onshore oil and gas has made rapid progress over the past decade, primarily due to the development of unconventional resources, particularly shale gas. − Although shale gas extraction has broken through the industrial capacity barrier, it is at the critical point of marginal benefits, making it difficult to achieve beneficial development. Shale gas production optimization is getting increasing attention in the oil industry.…”