Gem output winprop
An NPV of USD 611.22 million is obtained when no ML was used. 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. The results illustrate the ability of the developed LSTM model to accurately predict local gas demands during periods of high or low gas demand. The developed 32/256/128/120 LSTM model showed at least a 93% (61%) prediction performance using three or five input features. After ranking F1 scores, the developed NN outperforms the RF and support-vector-machine (SVM) algorithms for frac/refrac-well classification. 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. A water-management structure is also developed for the optimization framework. 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. Using the obtained results, best-practice field-development strategies are implemented in the area of interest (AOI) using reservoir simulation. 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. 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. This SP model uses a mixed-integer-nonlinear-programming (MINLP) formulation developed in the General Algebraic Modeling System (GAMS, Release 27.). 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. 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. Moreover, operators must bear in mind the undulating natural-gas demands persisting in an oversupplied shale-gas environment. As field-development strategies continue to evolve, refracturing and infill-well drilling must be carefully combined to optimize shale-project profitability. 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.