An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification

Abstract

Summary The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic–fracturing operations in liquid–rich shale plays in North America. As field–development strategies continue to evolve, refracturing and infill–well drilling must be carefully combined to optimize shale–project profitability. Moreover, operators must bear in mind the undulating natural–gas demands persisting in an oversupplied shale–gas environment. In this paper, we use data–driven approaches to predict successful refracturing candidates and local gas demand for the second–tier optimization of a shale–gas supply–chain network. A strategic–planning (SP) model is developed for optimizing the net present value (NPV) of a case–study shale–gas network in the Marcellus Play. This SP model uses a mixed–integer–nonlinear–programming (MINLP) formulation developed in the General Algebraic Modeling System (GAMS, Release 27.1.0.2019). This model relies directly on input from reservoir simulation, local–gas–demand forecast, water–availability forecast, and natural–gas and West Texas Intermediate (WTI) crude–oil price forecasts. Before reservoir simulation, machine learning (ML) is used to predict successful refracturing candidates, using a feed–forward neural network (NN), random–forest (RF) classifier, and a t–distributed stochastic–neighbor–embedding (t–SNE) visualization technique. Using the obtained results, best–practice field–development strategies are implemented in the area of interest (AOI) using reservoir simulation. Local gas demand is forecasted using a long–short–term–memory (LSTM) recurrent NN (RNN) that uses a multivariate data set created from local and global variables affecting shale–gas demand. A water–management structure is also developed for the optimization framework. Using a 300–well data set (with 17 input features), successful refracturing candidates were proposed according to the joint outcome of an optimal 17/23/128/2 feed–forward NN, a t–SNE plot, and a techno–economic review. After ranking F1 scores, the developed NN outperforms the RF and support–vector–machine (SVM) algorithms for frac/refrac–well classification. The developed 32/256/128/120 LSTM model showed at least a 93% (±1%) prediction performance using three or five input features. The results illustrate the ability of the developed LSTM model to accurately predict local gas demands during periods of high or low gas demand. After SP optimization over a 10–year planning horizon, the economic results indicate an NPV of USD 481.945 million, using the proposed physics–data–driven–based approach. An NPV of USD 611.22 million is obtained when no ML was used. The results reveal that the application of ML to strategic planning can prevent erroneous feedback of project profitability while allowing early–time decision making that maximizes shale–asset NPV.