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     

Optimal design of water pipeline networks for the development of shale gas resources

One of the major concerns in shale gas production is water management. Millions of gallons of water are injected to fracture each well and a significant amount returns to the surface as flowback. Operators are increasingly reusing flowback to reduce freshwater consumption and impaired water disposal. Because of this, networks of water pipelines in U.S. shales are growing fast. This work is aimed at addressing the optimal planning of shale gas operations in multiple wellpads together with the design of water distribution networks (WDN). We propose a multiperiod mixed-integer linear programming model to solve the challenging stay-or-mobilize trade-off. The proposed model permits to schedule operations at a detailed level, accounting for the WDN required to maximize the reuse of impaired water. We present illustrative examples involving up to 20 pads, 4 frac-crews, and 100 wells developed over 1 year, showing that the design of the WDN can be effectively optimized.     

Supply chain networks servicing upstream operations in oil and gas fields after the shale revolution

The recent irruption of shale resources in the oil and gas (O&G) industry has dramatically changed the paradigm for managing upstream operations. Unconventional production is largely driven by drilling new wells, yielding a much larger scale of material and service flows that need to be efficiently planned. This article presents an optimization framework for the design of integrated supply chain networks (SCNs) servicing upstream operations. Novel aspects addressed in this work are the integrated planning of material and service supplies; the economies of scale in operating, transportation and capital expenditures; the concept of waiting cost to quantify production losses; and the reverse flow of materials within closed-loop supply chains. The merits of the proposed approach are assessed through real-world case studies from the Argentine O&G industry. Optimal designs yield significant cost savings and confirm that the integrated planning of the SCN is critical for O&G operators after the shale revolution.     

Investment optimization model for freshwater acquisition and wastewater handling in shale gas production

Major challenges of water use in the drilling and fracturing process in shale gas production are large volumes required in a short‐period of time and the nonsteady nature of wastewater treatment. A new mixed‐integer linear programming (MILP) model for optimizing capital investment decisions for water use for shale gas production through a discrete‐time representation of the State‐Task Network is presented. The objective is to minimize the capital cost of impoundment, piping, and treatment facility, and operating cost including freshwater, pumping, and treatment. The goal is to determine the location and capacity of impoundment, the type of piping, treatment facility locations and removal capability, freshwater sources, as well as the frac schedule. In addition, the impact of several factors such as limiting truck hauling and increasing flowback volume on the solution is examined. A case study is optimized to illustrate the application of the proposed formulation. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1770–1782, 2015     

Strategic planning,design, and development of the shale gas supply chain network

The long‐term planning of the shale gas supply chain is a relevant problem that has not been addressed before in the literature. This article presents a mixed‐integer nonlinear programming (MINLP) model to optimally determine the number of wells to drill at every location, the size of gas processing plants, the section and length of pipelines for gathering raw gas and delivering processed gas and by‐products, the power of gas compressors, and the amount of freshwater required from reservoirs for drilling and hydraulic fracturing so as to maximize the net present value of the project. Because the proposed model is a large‐scale nonconvex MINLP, we develop a decomposition approach based on successively refining a piecewise linear approximation of the objective function. Results on realistic instances show the importance of heavier hydrocarbons to the economics of the project, as well as the optimal usage of the infrastructure by properly planning the drilling strategy. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2122–2142, 2014     

Optimization models for shale gas water management

There are four key aspects for water use in hydraulic fracturing, including source water acquisition, wastewater production, reuse and recycle, and subsequent transportation, storage, and disposal. Water use life cycle is optimized for wellpads through a discrete‐time two‐stage stochastic mixed‐integer linear programming model under uncertain availability of water. The objective is to minimize expected transportation, treatment, storage, and disposal cost while accounting for the revenue from gas production. Assuming freshwater sources, river withdrawal data, location of wellpads, and treatment facilities are given, the goal is to determine an optimal fracturing schedule in coordination with water transportation, and its treatment and reuse. The proposed models consider a long‐time horizon and multiple scenarios from historical data. Two examples representative of the Marcellus Shale play are presented to illustrate the effectiveness of the formulation, and to identify optimization opportunities that can improve both the environmental impact and economical use of water. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3490–3501, 2014     

An optimization framework for the integration of water management and shale gas supply chain design

This study presents the mathematical formulation and implementation of a comprehensive optimization framework for the assessment of shale gas resources. The framework simultaneously integrates water management and the design and planning of the shale gas supply chain, from the shale formation to final product demand centers and from fresh water supply for hydraulic fracturing to water injection and/or disposal. The framework also addresses some issues regarding wastewater quality, i.e., total dissolved solids (TDS) concentration, as well as spatial and temporal variations in gas composition, features that typically arise in exploiting shale formations. In addition, the proposed framework also considers the integration of different modeling, simulation and optimization tools that are commonly used in the energy sector to evaluate the technical and economic viability of new energy sources. Finally, the capabilities of the proposed framework are illustrated through two case studies (A and B) involving 5 well-pads operating with constant and variable gas composition, respectively. The effects of the modeling of variable TDS concentration in the produced wastewater is also addressed in case study B.     

Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi‐scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi‐level mixed‐integer linear programming model is solved by a decomposition‐based column‐and‐constraint generation algorithm. To illustrate the application, a county‐level case study on optimal design and operations of a spatially‐explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016     

Economic and environmental strategic water management in the shale gas industry: Application of cooperative game theory

In this work, a mixed-integer linear programming (MILP) model is developed to address optimal shale gas-water management strategies among shale gas companies that operate relatively close. The objective is to compute a distribution of water-related costs and profit among shale companies to achieve a stable agreement on cooperation among them that allows increasing total benefits and reducing total costs and environmental impacts. We apply different solution methods based on cooperative game theory: The Core, the Dual Core, the Shapley value, and the minmax Core. We solved different case studies including a large problem involving four companies and 207 wells. In this example, individual cost distribution (storage cost, freshwater withdrawal cost, transportation cost, and treatment cost) assigned to each player is included. The results show that companies that adopt cooperation strategies improve their profits and enhance the sustainability of their operations through the increase in recycled water.     

Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm

The optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR) is addressed. A two‐stage stochastic mixed‐integer linear fractional programming (SMILFP) model is developed to optimize the levelized cost of energy generated from shale gas. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end‐uses, and transportation. To reduce the model size and number of scenarios, we apply a sample average approximation method to generate scenarios based on the real‐world EUR data. In addition, a novel solution algorithm integrating the parametric approach and the L‐shaped method is proposed for solving the resulting SMILFP problem within a reasonable computational time. The proposed model and algorithm are illustrated through a case study based on the Marcellus shale play, and a deterministic model is considered for comparison. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3739–3755, 2015     

Game theory approach to optimal design of shale gas supply chains with consideration of economics and life cycle greenhouse gas emissions

This article addresses the optimal design of a non‐cooperative shale gas supply chain based on a game theory approach. Instead of assuming a single stakeholder as in centralized models, we consider different stakeholders, including the upstream shale gas producer and the midstream shale gas processor. Following the Stackelberg game, the shale gas producer is identified as the leader, whose objectives include maximizing its net present value (NPV) and minimizing the life cycle greenhouse gas (GHG) emissions. The shale gas processor is identified as the follower that takes actions after the leader to maximize its own NPV. The resulting problem is a multiobjective mixed‐integer bilevel linear programming problem, which cannot be solved directly using any off‐the‐shelf optimization solvers. Therefore, an efficient projection‐based reformulation and decomposition algorithm is further presented. Based on a case study of the Marcellus shale play, the non‐cooperative model not only captures the interactions between stakeholders but also provides more realistic solutions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2671–2693, 2017     

Toward more cost‐effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation‐based optimization

A novel process design for a more cost‐effective, greener process for making chemicals from shale gas and bioethanol is presented. The oxidative coupling of methane and cocracking technologies are considered for converting methane and light natural gas liquids, into value‐added chemicals. Overall, the process includes four process areas: gas treatment, gas to chemicals, methane‐to‐ethylene, and bioethanol‐to‐ethylene. A simulation‐optimization method based on the NSGA‐II algorithm for the life cycle optimization of the process modeled in the Aspen HYSYS is developed. An energy integration model is also fluidly nested using the mixed‐integer linear programming. The results show that for a “good choice” optimal design, the minimum ethylene selling price is $655.1/ton and the unit global‐warming potential of ethylene is 0.030 kg CO₂‐eq/kg in the low carbon shale gas scenario, and $877.2/ton and 0.360 kg CO₂‐eq/kg in the high carbon shale gas scenario. © 2014 American Institute of Chemical Engineers AIChE J, 61: 1209–1232, 2015     

Risk management for a global supply chain planning under uncertainty: Models and algorithms

In this article, we consider the risk management for mid‐term planning of a global multi‐product chemical supply chain under demand and freight rate uncertainty. A two‐stage stochastic linear programming approach is proposed within a multi‐period planning model that takes into account the production and inventory levels, transportation modes, times of shipments, and customer service levels. To investigate the potential improvement by using stochastic programming, we describe a simulation framework that relies on a rolling horizon approach. The studies suggest that at least 5% savings in the total real cost can be achieved compared with the deterministic case. In addition, an algorithm based on the multi‐cut L‐shaped method is proposed to effectively solve the resulting large scale industrial size problems. We also introduce risk management models by incorporating risk measures into the stochastic programming model, and multi‐objective optimization schemes are implemented to establish the tradeoffs between cost and risk. To demonstrate the effectiveness of the proposed stochastic models and decomposition algorithms, a case study of a realistic global chemical supply chain problem is presented. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain

The design and planning of an integrated ethanol and gasoline supply chain is addressed, and is composed of harvesting and production sites for ethanol, petroleum refineries, distribution centers where blending takes place, and the retail gas stations where blends of gasoline and ethanol are sold. We postulate a superstructure that combines all the components of the supply chain and different means of transportation, and model this multiscale design problem as a multiperiod MILP model. In order to identify regions where investments are needed and the optimal configuration of the network, a strategic planning model is considered in which gasoline stations are aggregated in different regions. A detailed formulation is considered where regions are disaggregated into gas stations to determine the retrofit projects for the selection of blending pumps over their expected life. Also, the application of these MILP models with two large‐scale problems are illustrated. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4655–4672, 2013     

基于环境风险评价的页岩气水务管理框架及其模块分析

随着全球页岩气勘探开发的快速推进,页岩气开发的诸多环境问题也逐渐凸显,缺乏相应的环境评价和管理方法,尤其是在水务管理方面。提出了基于环境风险评价的页岩气水务管理框架;从钻井、水力压裂、采出水处理等3个阶段系统阐述和分析了页岩气水务管理中存在的风险源项,包括其污染通道和种类;结合环境风险评价方法的优缺点评估和应用前景,推荐采用故障树/事故树分析、概率风险评价法以及基于概率理论统计和模糊集的综合法进行页岩气水务管理风险评价。     

Balancing responsiveness and economics in process supply chain design with multi‐echelon stochastic inventory

This article is concerned with the optimal design of multi‐echelon process supply chains (PSCs) under economic and responsive criteria with considerations of inventory management and demand uncertainty. The multi‐echelon inventory systems are modeled with the guaranteed service approach to handle the uncertain demands at each echelon. The maximum guaranteed service time of the last echelon of the PSC is proposed as a measure of a PSC's responsiveness. The problem is formulated as a bi‐criterion mixed‐integer nonlinear program (MINLP) with the objectives of minimizing the annualized cost (economic objective) and minimizing the maximum guaranteed service times of the markets (responsiveness objective). The model simultaneously predicts the optimal network structure, transportation amounts, and inventory levels under different specifications of the PSC responsiveness. An example on acetic acid supply chain is presented to illustrate the application of the proposed model and to comprehensively compare different measures of PSC responsiveness. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Global superstructure optimization for the design of integrated process water networks

We propose a general superstructure and a model for the global optimization for integrated process water networks. The superstructure consists of multiple sources of water, water‐using processes, wastewater treatment, and pre‐treatment operations. Unique features are that all feasible interconnections are considered between them and multiple sources of water can be used. The proposed model is formulated as a nonlinear programing (NLP) and as a mixed integer nonlinear programing (MINLP) problem for the case when 0–1 variables are included for the cost of piping and to establish optimal trade‐offs between cost and network complexity. To effectively solve the NLP and MINLP models to global optimality we propose tight bounds on the variables, which are expressed as general equations. We also incorporate the cut proposed by Karuppiah and Grossmann to significantly improve the strength of the lower bound for the global optimum. The proposed model is tested on several examples. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

石油供应链计划层优化与不确定性风险管理模型

提出了一种基于有限场景的两阶段随机混合整数线性规划（MILP）模型,来优化不确定条件下多周期、多层级的石油工业供应链的计划层管理。模型以碳排放税的形式将减少CO₂排放的环境目标融入到经济目标之中。供应链的各级节点均以黑箱的形式存在,使模型得以简化,在时间尺度相对较长的计划层获得优化结果,为供应链的计划与管理提供指导方案。并且分析了算例最优期望收益的风险性,在此基础上引入风险管理约束,得到了带有风险管理约束的供应链计划层优化模型。该模型的结果与原模型相比,期望收益附近的收益风险性降低。     

Multi‐period planning,design, and strategic models for long‐term,quality‐sensitive shale gas development

In this work we address the long‐term, quality‐sensitive shale gas development problem. This problem involves planning, design, and strategic decisions such as where, when, and how many shale gas wells to drill, where to lay out gathering pipelines, as well as which delivery agreements to arrange. Our objective is to use computational models to identify the most profitable shale gas development strategies. For this purpose we propose a large‐scale, nonconvex, mixed‐integer nonlinear programming model. We rely on generalized disjunctive programming to systematically derive the building blocks of this model. Based on a tailor‐designed solution strategy we identify near‐global solutions to the resulting large‐scale problems. Finally, we apply the proposed modeling framework to two case studies based on real data to quantify the value of optimization models for shale gas development. Our results suggest that the proposed models can increase upstream operators’ profitability by several million U.S. dollars. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2296–2323, 2016     

Sustainable management and design of the energy-water-food nexus using a mathematical programming approach

Global primary and secondary resources are important for economic growth. Resource management and environment conservation are currently frequently discussed topics worldwide. In this study, a discrete optimization model formulation is presented for an integrated energy, water, and food (EWF) supply chain problem. The optimization model examines the temporal and spatial integration of the EWF supply chain elements to provide optimal infrastructure capacity expansion of essential commodities within the EWF system, and their corresponding periodic optimal supply for a given region. Furthermore, the model considers endogenous demand between the EWF elements that reflect the interdependency of nexus elements. A mixed integer linear programming model is developed to assist in the process of optimal infrastructure capacity expansion and operation of the EWF system. A case study is given to show the application of the proposed mathematical programming model. Several scenarios are assumed for the case study under different commodity prices and climate change conditions. In addition, diversification in the energy and agriculture sectors is examined by shifting from international refined sugar trading to bioethanol production. The results show economic gains of ~10% under the emergence of bioethanol production compared with the business-as-usual scenario. Production dynamic exits for the production of refined sugar, bioethanol, and power from sugarcane and bagasse resources over time in the considered sale price range for the refined sugar and bioethanol products.