Exploring the benefits of utilizing small modular device for sustainable and flexible shale gas water management

Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains

Evaluating the spatiotemporal variability of water recovery ratios of shale gas wells and their effects on shale gas development

This paper aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions as well as second-stage operational decisions. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a case study of Marcellus shale gas supply chain. Comparisons with deterministic optimization models are investigated as well.

This work develops a novel dynamic material flow analysis (MFA)-based optimisation modelling framework for sustainable design of shale gas energy systems. This dynamic MFA-based framework provides high-fidelity modelling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. Moreover, by incorporating a dimension of resource sustainability, the proposed modelling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimisation problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modelling framework and solution algorithm, a case study of Marcellus shale gas supply chain is presented. The optimisation results help to identify clear trade-offs among economic, environmental, and resource performances in the shale gas energy system, and the MFA-based framework offers an effective way to find an optimal solution with well-balanced sustainability perfor

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.

Data driven techno-economic framework for the development of shale gas resources

Optimal design of shale gas supply chain network considering MPC-based pumping schedule of hydraulic fracturing in unconventional reservoirs

Multi-system shale gas supply chain planning with development and resource arrangements

Shale gas energy is the most prominent and dominating source of power across the globe. The processes for the extraction of shale gas from shale rocks are very complex. In this study, a multiobjective optimization framework is presented for an overall water management system that includes the allocation of freshwater for hydraulic fracturing and optimal management of the resulting wastewater with different techniques. The generated wastewater from the shale fracking process contains highly toxic chemicals. The optimal control of a massive amount of contaminated water is quite a challenging task. Therefore, an on-site treatment plant, underground disposal facility, and treatment plant with expansion capacity were designed to overcome environmental issues. A multiobjective trade-off between socio-economic and environmental concerns was established under a set of conflicting constraints. A solution method—the neutrosophic goal programming approach—is suggested, inspired by independent, neutral/indeterminacy thoughts of the decision-maker(s). A theoretical computational study is presented to show the validity and applicability of the proposed multiobjective shale gas water management optimization model and solution procedure. The obtained results and conclusions, along with the significant contributions, are discussed in the context of shale gas supply chain planning policies over different time horizons.

This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two‐stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first‐stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second‐stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data‐driven approach is applied to construct the ambiguity set based on principal component analysis and first‐order deviation functions. By taking advantage of affine decision rules, a tractable mixed‐integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small‐scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. © 2018 American Institute of Chemical Engineers AIChE J, 65: 947–963, 2019

The development of shale gas resources is subject to technical challenges and markedly affected by volatile markets that can undermine the development of new projects. Consequently, stakeholders can greatly benefit from decision‐making support tools that integrate the complexity of the system along with the uncertainties inherent to the problem. Accordingly, a general methodology is proposed in this work for the evaluation of integrated shale gas and water supply chains under uncertainty. First, key parametric uncertainties are identified from a candidate pool via a global sensitivity analysis based on a deterministic optimization model. Then, a two‐stage stochastic model is developed considering only the key uncertain parameters in the problem. Moreover, the merits of modeling uncertainty and implementing the stochastic solution approach are evaluated using the expected value of perfect information and the value of the stochastic solution metrics. Furthermore, the conditional value‐at‐risk approach was implemented to evaluate different risk‐aversion levels and the corresponding impacts on the shale gas development plan. The proposed methodology is illustrated through two real‐world case studies involving six and eight potential well‐pad locations and two options of well‐pad layouts. © 2018 American Institute of Chemical Engineers AIChE J, 65: 924–936, 2019

In this paper, we address the sustainable design and operations of shale gas supply chains by proposing an integrated hybrid life cycle optimization (LCO) modelling framework. Unlike the traditional process-based LCO that suffers system truncation, the integrated hybrid LCO supplements the truncated system with a comprehensive economic input-output system. Meanwhile, the integrated hybrid LCO retains the precision in modelling major unit processes within the well-to-wire system boundary compared with the economic input-output-based LCO models. With the help of the integrated hybrid LCO framework, we can automatically identify the optimal sustainable alternatives in the design and operations of shale gas supply chains. To demonstrate the applicability, we present a case study of a well-to-wire shale gas supply chain located in the UK. According to the optimization results, the lowest levelized cost of electricity generated from shale gas is £51.8 /MWh, and the optimal life cycle GHG emissions are 473.5 kg CO2-eq/MWh.

Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, a novel game theory based stochastic model is proposed that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover’s linearization method. To illustrate the applicability of proposed modeling framework, a case study of a large-scale shale gas supply chain is presented, which demonstrates the advantages of the proposed modeling framework and efficiency of the solution algorithm.

Energy-environmental implications of shale gas extraction with considering a stochastic decentralized structure

A stochastic game theoretic framework for decentralized optimization of multi-stakeholder supply chains under uncertainty

A three-level framework for balancing the tradeoffs among the energy, water, and air-emission implications within the life-cycle shale gas supply chains

This paper analyzes the life cycle environmental impacts of shale gas by using an integrated hybrid life cycle analysis (LCA) and optimization approach. Unlike the process-based LCA that suffers system truncation, the integrated hybrid LCA supplements the truncated system with a comprehensive economic input-output system. Compared with the economic input–output-based LCA that loses accuracy from process aggregation, the integrated hybrid LCA retains the precision in modeling major unit processes within the well-to-wire system boundary. Three environmental categories, namely, life cycle greenhouse gas emissions, water consumption, and energy consumption, are considered. Based on this integrated hybrid LCA framework, we further developed an integrated hybrid life cycle optimization model, which enables automatic identification of sustainable alternatives in the design and operations of shale gas supply chains. We applied the model to a well-to-wire shale gas supply chain in the UK to illustrate the applicab...

Multi-criteria design of shale-gas-water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty

Modular manufacturing is identified to have great potential in the exploitation of shale gas resource. In this work, we propose a novel mixed-integer nonlinear fractional programming model to investigate the economic and environmental implications of incorporating modular manufacturing into well-to-wire shale gas supply chains. Both design and operational decisions regarding modular manufacturing are considered, including modular plant allocation, capacity selection, installment planning, moving scheduling, and salvage operation, as well as other decisions for shale gas supply chain design and operations, such as drilling schedule, water management, and pipeline network construction. To systematically evaluate the full spectrum of environmental impacts, an endpoint-oriented life cycle optimization framework is applied that accounts for up to 18 midpoint impact categories and three endpoint impact categories. Total environmental impact scores are obtained to evaluate the comprehensive life cycle environmen...