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Abstract

Shale gas has emerged as a viable source of clean energy with advances in horizontal drilling and multi-stage fracturing. Well performance in the ultra-low permeability shale gas is governed by the interaction between reservoir rock and the fractures created during hydraulic fracturing. Numerical modeling historically has been used to study impact of variation in reservoir properties and completion characteristics on well performance. Shale gas field development requires a large number of wells to be drilled. The inordinate resource requirement to numerically model each well, necessitates the development of quick diagnostic tools. This paper explores how readily available pressure and rate data can be utilized to estimate and understand the unknowns involved in the completion and the reservoir parameters. Synthetic models have been used to generate well performance history which was then diagnosed using an analytical modeling workflow to generate well performance signatures. The synthetic models provided a controlled basis to study the reservoir and completion phenomena which are difficult to measure in the real field data. In practical application, multiple unknowns can co-exist to impact the overall well performance. Some of the major unknowns that were studied are reservoir permeability, fracture spacing, fracture half-length, fracture and matrix permeability changes with pressure. This paper also captures the performance signature changes with variation in original gas in place (OGIP) and the impact of contribution from stimulated rock volume (SRV) and external rock volume (XRV) outside the SRV. Fracture geometry is rarely known with certainty so this paper also addresses the performance changes that can be observed for various fracture geometry realization while conserving the total fracture area in all the models. The final section of the paper will address understanding the impact on estimated ultimate recovery (EUR) forecast for a particular scenario and how error in estimating the same reduces over time. We believe this fingerprint catalogue will serve as a valuable resource for prompt identification of dominant flow mechanism while providing a diagnostic method for identifying key indicators that control overall well performance.

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/content/papers/10.3997/2214-4609-pdb.350.iptc16396
2013-03-26
2020-04-09
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.350.iptc16396
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