Optimal planning of artificial lift operations in a shale gas multiwell pad

Lifting operations play a key role to sustain shale gas wells productivity. Artificial Lift Systems (ALS) involve a wide range of techniques aimed at deliquifying wells. Although these operations are relatively mature for vertical wells in conventional reservoirs, enhancing productivity from shale gas wells has become a new frontier for unconventional production. The main contribution of this work is the development of the first Mixed Integer Linear Programming (MILP) formulation determining the integrated planning of several ALS in a multiwell pad. The model simultaneously manages ALS selection, investment and operational decisions, hedging against rapid decline curves in different wells. We introduce very detailed piecewise functions accounting for the ALS installation and removal times. The performance of the MILP is evaluated by solving five illustrative case studies.     

Multi-period design and planning model of shale gas field development

Long-term design and planning of shale gas field development is challenging due to the complex development operations and a wide range of candidate locations. In this work, we focus on the multi-period shale gas field development problem, where the shale gas field has multiple formations and each well can be developed from one of several alternative pads. The decisions in this problem involve the design of the shale gas network and the planning of development operations. A mixed-integer linear programming (MILP) model is proposed to address this problem. Since the proposed model is a large-scale MILP, we propose a solution pool-based bilevel decomposition algorithm to solve it. Results on realistic instances demonstrate the value of the proposed model and the effectiveness of the proposed algorithm.     

Optimization models for planning shale gas well refracture treatments

Refracturing is a promising option for addressing the characteristically steep decline curves of shale gas wells. In this work we propose two optimization models to address the refracturing planning problem. First, we present a continuous‐time nonlinear programming model based on a novel forecast function that predicts pre‐ and post‐treatment productivity declines. Next, we propose a discrete‐time, multi‐period mixed‐integer linear programming (MILP) model that explicitly accounts for the possibility of multiple refracture treatments over the lifespan of a well. In an attempt to reduce solution times to a minimum, we compare three alternative formulations against each other (big‐M formulation, disjunctive formulation using Standard and Compact Hull‐Reformulations) and find that the disjunctive models yield the best computational performance. Finally, we apply the proposed MILP model to two case studies to demonstrate how refracturing can increase the expected recovery of a well and improve its profitability by several hundred thousand USD. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4297–4307, 2016     

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     

Stochastic programming models for optimal shale well development and refracturing planning under uncertainty

In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, and whether or not it should be refractured over its lifespan. We account for exogenous gas price uncertainty and endogenous well performance uncertainty. We propose a mixed‐integer linear, two‐stage stochastic programming model embedded in a moving horizon strategy to dynamically solve the planning problem. A generalized production estimate function is described that predicts the gas production over time depending on how often a well has been refractured, and when exactly it was restimulated last. From a detailed case study, we conclude that early in the life of an active shale well, refracturing makes economic sense even in low‐price environments, whereas additional restimulations only appear to be justified if prices are high. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

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     

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     

页岩气水平井小层钻遇率与产量差异分析

地质因素和工程因素是非常规储层开发评价的关键技术指标。为此,选取工程质量相近和生产制度相近的水平井,结合地质沉积、取芯测井资料,储层小层钻遇率等资料综合分析。结果表明:储层小层钻遇率是影响页岩气产量的主要地质因素,通过分析结果认识,对下一步页岩气开发提产增效具有一定指导意义。     

Optimal planning of oil and gas development projects considering long-term production and transmission

This paper proposes an integrated model for making a group of strategic decisions about oil and gas development projects simultaneously over a long-term planning horizon. These decisions involve: selection of field and pipeline development projects, scheduling of selected projects, production planning, and upstream transmission planning. The proposed model is formulated as a linear mixed-integer-programming model. It is implemented in a case study to demonstrate its usefulness and applicability in practice. Finally, a number of sensitivity analyses are carried out to analyze the impact of most influential uncertainties on the solutions and the corresponding results are discussed.     

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