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

Abstract

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.

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

“…Similar results have also been found for nonconventional wells [126,127]. Drouven and Grossmann [128] have applied Generalized Disjunctive Programming (GDP) coupled with a tailored solution method utilizing gradient based optimizers to well location optimization of shale gas wells combined with surface gathering system design and operational considerations. They found that profitability of investments could be significantly improved by combining well location and surface gathering considerations.…”

Development of Shale Gas Supply Chain Network under Market Uncertainties

“…This assumption differs from some previous approaches, which considered variability of composition in different shale sites [6,16,17]; (2) The number of wells that can be drilled and hydro-fracture in a shale site in each time period is bounded. Moreover, the maximum number of wells that can be drilled in each shale site throughout the planning horizon is also known beforehand; (3) Multiple wells in the same shale site can be drilled, hydro-fractured, and completed in the same period; (4) A quarterly discretization is considered for the planning horizon of the shale gas project; (5) Well productivity rate is formulated based on the well age; (6) Flowback water represents a fraction of the fracking water utilized during the hydraulic operations in each shale site; (7) Produced water in different shale sites is proportional to the shale gas production in that site; (8) Different management options can be utilized to handle the wastewater generated in each shale site due to the hydro-fracturing activities; (9) Shale sites are located in a region without the necessary pipeline and processing infrastructure.…”

MOSAIC – Enabling Large‐Scale Equation‐Based Flow Sheet Optimization

“…Whilst C 2 H 4 is the desired product, the stand-alone optimization of the OCM reactor to maximize the yield in C 2 H 4 causes misleading results. The main issues are that (1) in states of high yield the concentration of the main by-product CO 2 might also be high, (2) in cases of high selectivity major parts of the reactant CH 4 remain unreacted and hence need to be separated afterwards, or (3) the dilution with inert gases (CO 2 or N 2 ) is so high that the product separation becomes economically infeasible. For these reasons the whole process concept containing reaction and product separation needs to be optimized simultaneously.…”

“…The last 35 years have brought major advancements in the size and complexity of flow sheets, the incorporation of thermodynamic models, numerical solutions, and graphical user interfaces . By now, there are also numerous interactions between operations research for optimal planning and scheduling of chemical sites , product changeover for batch productions as well as real‐time optimization with large‐scale process simulation tasks . However, up to now, really challenging process simulation and process optimization tasks, e.g., of systems with complex electrolytes and reactions, are seldom carried out in the same tool or programming language: instead either sequential optimization schemes are commonly set up or surrogate models are developed based on rigorous simulations, which are then implemented as part of the optimization problem .…”