Shale Gas Supply Chain Research Articles

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

Growing shale gas extraction in recent years has triggered wide discussions on the associated freshwater requirement and wastewater management. Many optimization approaches have been developed for shale gas water management; however, most of the studies assumed permanent utilization of wastewater treatment facilities with fixed capacities. Considering the rapidly declining characteristics of shale gas wastewater production, these treatment facilities could remain largely underutilized after the first few months/years of production, making them less economically attractive. To maximize the capacity utilization of treatment facilities and further improve the economic performance of shale gas development, this study develops a systematic optimization framework, where the capacity strategy of conventional treatment facilities and utilization of the recent concept of modular manufacturing are both considered for flexible shale gas water management. The proposed mixed-integer linear programming (MILP) model simultaneously optimizes the design and planning of integrated shale gas and water supply chain, with a focus on capacity planning for both large-scale conventional treatment facilities and small-scale modular devices. A series of Marcellus-based case studies are performed to illustrate the applicability of the proposed model and provide general insights into the trade-offs between the multiple types of treatment facilities. The optimization results reveal that the combinatorial utilization of conventional facilities and modular devices for wastewater treatment (66% by conventional facilities and 34% by modular devices) brings 9.3% more reused water for other well development and 6.2% savings in water-related costs, compared to flexible management of only conventional facilities. This work suggests that taking modular device as auxiliary equipment for shale gas water management is most beneficial to increase the capacity utilization of treatment facilities and achieve a more economic and sustainable shale gas production system.

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

Water management has increasingly become a hotspot in shale gas supply chains. This study develops a comprehensive modeling framework for the mixed noise-driven shale gas-water supply chains, which is integrated with techniques of system dynamics model and two-stage stochastic risk-aversion programming. White and colored noises are used for addressing stochastic characterization of shale gas productivity. Regional water resources carrying capacity is reflected based on the system dynamics model. The developed model cannot only effectively address stochastic parameters in the objective and constraints, but also offer a linkage between the pre-regulated policies and corresponding economic implications raised from improper policies. A Marcellus-based case in Pennsylvania is then performed to validate the applicability of the developed model. Results reveal that shale gas production curve is more sensitive to colored and mixed noises than white noise. High flowback and recycle rates would increase carrying capacity of shale wells by 1.8% in 2023. An increase in weighting factor (e.g., from 0 to 30) would induce a rise in system cost (e.g., from $1.22×1011 to $1.34×1011) but a reduction in conditional value-at-risk value (e.g., from $6.83×1010 to $3.83×1010). Weighting factor thus can be served as an indicator to show how much the decision makers’ attention to system risk.

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

Unconventional shale gas production in the United States has been largely improved due to the development of hydraulic fracturing technology and is projected to rapidly grow in the coming years. However, the acquisition of freshwater and management of flowback and produced (FP) water associated with hydraulic fracturing operation are two of the greatest challenges in shale gas development, especially in arid regions. For efficient and sustainable water management, a better understanding of freshwater consumption and FP water production for shale gas wells is necessary to appropriately expand and upgrade the existing water network and shale gas network. To achieve this, we first collected water-use volume and monthly FP water production volume data for shale gas wells drilled in the Eagle Ford and Marcellus shale regions. Next, after integrating the data from multiple database sources, the water recovery ratio was calculated as the ratio of cumulative FP water volume to water-use volume and used as a metric to characterize the wells in these two shale regions. Then, we analyzed the obtained water recovery ratio data according to the location and production history to study the spatiotemporal variations across multiple counties and time periods. It shows that around 30% of the collected wells drilled in the Eagle Ford region have the water recovery ratio greater than 1; however, only 1% of the collected wells drilled in the Marcellus region have the water recovery ratio greater than 1. Besides, the water recovery ratios vary significantly across the counties in each shale region. To demonstrate how different water recovery ratio may affect shale gas development, a shale gas supply chain network (SGSCN) optimization model from the literature was utilized to perform two case studies in the Marcellus region. The optimal results suggest that different configurations of SGSCN are required for economically desirable and practically feasible management of shale gas wells with different water recovery ratios.

A Data-Driven Optimization Approach for the Optimization of Shale Gas Supply Chains under Uncertainty

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.

Analysing Shale Gas Energy Systems using Dynamic Material Flow Analysis

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

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

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

This work presents a data driven techno-economic framework that combines upstream, midstream and downstream operations to optimize profitability of shale projects. This framework is illustrated using a shale gas supply chain structure planned for development and integration in an over-supplied gas market. Field development strategies are developed based on applying machine learning techniques to an existing field. Alternative development strategies are implemented on the integrated production-modeling platform RESOLVE–REVEAL to simulate hydrocarbon/water production. Long-short term memory (LSTM) neural networks are developed to predict gas demand and freshwater availability. Long-term strategic planning is achieved using a mixed-integer non-linear programming (MINLP) formulation. Results indicate a net present value (NPV) of 205.56 MMUS$ for optimal infrastructure design, gas and liquids transportation and distribution, and water management after 54-well integration. Additionlly, results provide an optimal gas storage schedule that supports shale asset profitability. Application of this techno-economic approach improves profitability projections for shale enterprises.

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

Several approaches have been developed to design shale gas supply chain network (SGSCN) in an economically viable manner, but the connection between hydraulic fracture geometry, shale gas production, and wastewater recovery has not received much attention. Specifically, the final fracture geometry in unconventional reservoirs significantly affects shale gas production rate and it indirectly determines the amount of recovered wastewater. To take into account the relations among hydraulic fracturing jobs, wastewater generation and management, and shale gas production and management, we focus on the development of a novel framework that integrates a model predictive control (MPC)-based pumping schedule of hydraulic fracturing and a SGSCN model. Based on this developed framework, the optimal SGSCN configuration, which maximizes the overall profit, is determined by solving a mixed-integer linear programming problem. The proposed SGSCN design framework is compared to the case when the same SGSCN model is integrated with a suboptimal pumping schedule like Nolte’s pumping schedule. The overall profit of the former is 4.49% higher than that of the latter.

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

In this paper, the optimal development of multiple shale gas systems and production and transportation of shale gas are studied. By tracing the path of shale gas from development to production to transportation to selling, a discrete-time mixed integer linear program (MILP) formulation is developed, which when solved, yields the economical scheduling of pad development operations and shale gas production, and planning of development resource and pipeline contracts. The MILP formulation is applied to two case studies to demonstrate the benefits of simultaneously optimizing development, production, and transportation for a multi-system shale gas development area.

Neutrosophic Optimization Model and Computational Algorithm for Optimal Shale Gas Water Management under Uncertainty

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.

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

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

Integrated shale gas supply chain design and water management under uncertainty

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

Sustainable Design and Operations of Shale Gas Supply Chains Using Integrated Hybrid Life Cycle Optimization Models

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.

Tackling Uncertain Performances of Multiple Stakeholders in the Design and Optimization of Decentralized Supply Chains

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

Major challenges in the shale gas supply chains are identification of the relationship between different stakeholders and evaluation of the environmental impacts under uncertainties. This study develops a stochastic decentralized fractional programming (SDFP) for the life cycle shale gas energy system planning, where the downstream optimization problem is treated as the upper-level model, and the upstream optimization problem is formulated as the lower-level model. Stochastic uncertainties in the estimated ultimate recovery (EUR) and greenhouse gas (GHG) emissions are considered into the decision making process. A SDFP based energy and environmental workflow is then formulated for a real-work case study of Marcellus shale play in Beaver County. Design and operational decisions for both leader and follower are generated in a sequential manner, involving well drilling schedule, energy flows, water resources management, and GHG emissions control. Results reveal that a higher certainty level of EUR value would correspond to a higher reliably in shale gas production, then to increased GHG emissions and economic benefits. Compared with the decentralized linear programs, the SDFP would provide more sustainable strategies, while the linear programs would generate either environment-oriented or economics-oriented strategies. These findings can help stakeholders to achieve the overall satisfaction of the supply chains and to provide useful information for regional GHG emissions control.

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

This paper investigates the influences of uncertainty in multi-stakeholder non-cooperative supply chains, and the corresponding optimal strategies based on game theory to hedge against uncertainty in design and operations of such decentralized supply chains. We propose a novel game-theory-based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme for optimizing decentralized supply chains under uncertainty. Both the leader's and the followers’ uncertainties are considered, which directly affect their design and operational decisions regarding infrastructure development, contracts selection, price setting, production profile, transportation planning, and inventory management. The resulting model is formulated as a two-stage 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. An illustrative example of flight booking under uncertain flight delays and a large-scale application to shale gas supply chains are presented to demonstrate the applicability of the proposed framework.

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

Two critical challenges, namely high water resources consumption and growing greenhouse gas (GHG) emissions, are encountered across the current shale gas supply chains. This study presents a three-level modeling framework for economic and environmental life-cycle optimization of the shale gas supply chains. Life cycle analysis (LCA) approach and Stackelberg leader-follower game are integrated into the optimization framework to account for a hierarchical structure.This hierarchical framework is capable of not only addressing the sequential decision-making problem raised by decision makers at different levels (e.g., the whole-system decision maker as a leader and the environment-development decision maker as a follower), but also developing multilevel cooperative control of water management and GHG-emission mitigation. An application to the Marcellus Shale is then given to demonstrate the capabilities of the developed three-level model. An improved leader-follower-interactive solution algorithm based on satisfactory degree is presented to tackle the computational challenge of the three-level program. The overall satisfaction solution is generated for satisfying the goals of different decision makers by compromising the trade-offs among energy, water, and air-emission implications. Optimal solutions with respect to well drilling schedule, shale gas production, freshwater supply, wastewater treatment, GHG emissions, and electricity generation would be obtained. These analyses are capable of helping decision makers adjust their tolerances to make informed decisions for the supply chains. Moreover, the decision making is not kept static but improved by repeatedly communicating with both different models and sensitivity analysis. Through the communications, the robustness and objectivity of the model solutions can further be enhanced.

Integrated Hybrid Life Cycle Assessment and Optimization of Shale Gas

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

One of the critical problems in cooperative shale gas supply chains and production systems design is life cycle optimization of the economic and environmental performance under uncertainty. This study develops an inexact multi-criteria decision making (IMCDM) model with consideration of shale gas production profiles and recoverable reserves. The IMCDM framework is based on an integration of life cycle analysis, interval linear programming, multi-objective programming, and multi-criteria decision analysis approaches. An application to the Marcellus Shale supply chains is presented to demonstrate capabilities and effectiveness of the developed model, where the future spread in shale gas output follows from the variation in drilled well counts according to different scenarios. Design and operational decisions with respect to well drilling schedule, shale gas production, freshwater supply, wastewater disposal, and greenhouse gas (GHG) emissions are then generated. An optimal strategy is further provided for stakeholders after evaluation of the trade-off among multiple criteria.

Can Modular Manufacturing Be the Next Game-Changer in Shale Gas Supply Chain Design and Operations for Economic and Environmental Sustainability?

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...