Engineering an Effective Completion and Stimulation Strategy for In-Fill Wells | SLB

Engineering an Effective Completion and Stimulation Strategy for In-Fill Wells

Published: 05/17/2017

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As the oil & gas industry enters into next phase of unconventional reservoir development, many new in-fill wells will be drilled in various shale oil and gas plays in North America. A detailed evaluation to devise an engineered approach for stimulating and completing these wells is critical to maximizing productivity. Challenging economics that prevail today have made it even more vital to perform such a study. This paper focuses on identifying optimum stimulation treatment design and completions strategy for the in-fill well. This work is a companion work to a paper presented by Gakhar et al. at the 2016 SPE/ CSUR Unconventional Resources Technology Conference (URTeC 2431182) on developing an engineered approach for multi-well pad development in Eagle Ford shale. Together these papers, serve as a comprehensive guide for multi-well pad performance optimization in unconventional reservoirs like the Eagle Ford Shale.

An ‘advanced integrated modeling workflow’ is used to execute the complex study. The workflow involves building a 3D structural geologic model based on a vertical openhole pilot well log in Eagle Ford shale reservoir. A discrete fracture network (DFN) is built from 3D seismic data interpretation. Hydraulic fracture treatment pumped on a parent well is simulated using ‘unconventional fracture model’ (UFM). The UFM simulates complex fractures, while honoring the interaction between hydraulic fractures and natural fractures. A dynamic grid with unstructured cells is then created. Hydrocarbon production from the parent well is simulated for a period of 400 days. A geomechanical finite element model (FEM) based simulator that is fully coupled with a 3D numerical reservoir simulator is then used to calculate spatial and temporal changes in in-situ stresses. Dynamic reservoir properties in the 3D model are then updated and the child well, which is drilled 600 ft away from the parent well, is built into the model. The UFM is used to simulate an array of stimulation treatment designs and compare alternate completions strategies for the child well. The reservoir simulator is then used to compare production performance of the alternate strategies. Note that in this paper, the terms "in-fill well" and "child well" are used interchangeably.

Extensive evaluation is carried out using the advanced integrated modeling workflow to achieve three key objectives. The first key objective is to determine an appropriate hydraulic fracturing treatment design for an in-fill well. Four hydraulic fracture treatment designs based on slickwater, delayed borate crosslinked gels, hybrid fluid treatments, and fiber based channel fracturing fluids for the in-fill well are compared. It has been found that under reservoir conditions specific to this study, the child well produces 22% more oil, if stimulated using the fiber based channel fracturing fluid than, if fractured using the slickwater. The second key objective is to compare the impact of refracturing and recharging the parent well prior to fracturing the child well. For the study well, refracturing increases oil production from the multi-well pad by 11% over the scenario, in which the parent well is recharged by injecting 43,200 bbl of water. The third objective of this study pertains to comparing the traditional plug-and-perf completions design with an alternate based on coupling plug-and-perf with a novel "sequenced fracturing" technique with a degradable fiber based fluid diversion blend for the child well. It has been found that by using the latest sequenced fracturing technique oil production from the multi-well pad can be increased by 14% over a scenario in which the child well is completed using traditional plug-and-perf design, despite pumping fewer stages on the well. The novel completion technique also greatly improves the efficiency of operation and provides significant savings on completions cost.

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