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Pressure transient analysis of commingled multilayer reservoirs: A deconvolution-based approach for individual layer characterization using sparse PLT data
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Mina S. Khalaf
Petroleum Research | 2026, 11(2) : 574 - 589
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Petroleum Research | 2026, 11(2): 574-589
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Pressure transient analysis of commingled multilayer reservoirs: A deconvolution-based approach for individual layer characterization using sparse PLT data
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Mina S. Khalaf
Affiliations
  • Cairo University, Egypt
Published: 2026-06-10 doi: 10.1016/j.ptlrs.2025.11.006
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Layering is common in hydrocarbon reservoirs due to stratified sedimentary deposition, with each layer exhibits distinct characteristics. Multilayer reservoirs are typically clasifiedas systems with interlayer/formation crosflow and systems without interlayer crosflow (commingled systems). This study focuses on the latter, where fluid exchange between layers occurs solely through the wellbore. Accurately interpreting pressure transient data from commingled systems to extract individual layer properties remains a persistent challenge. Most existing interpretation techniques rely on assumed reservoir models (typically homogeneous, isotropic, and infinite in extent) that involve numerous unknown parameters (such as permeabilities and skin factors) and require nonlinear history matching. These assumptions not only introduce subjectivity but can also yield unreliable estimates. The most reliable way to characterize individual layers has traditionally required isolating and testing each layer separately, which is a process that is both technically complex and costly to implement. This study introduces a testing and analysis methodology developed specificaly for commingled multilayer reservoirs. The proposed approach utilizes deconvolution to remove rate variation effects from bottomhole pressure data and generate pressure responses equivalent to constant-rate conditions. This transformation enables the recovery of distinct pressure signatures for each individual layer without requiring assumptions. The recovered pressure signals are wellbore storage (WBS) free and extend over the entire test duration, offering a clearer window into the dynamic behavior of each contributing formation. Deconvolution needs complete sandface rate data for each layer, data that are typically unavailable in field practice. To address this, the study develops a simple yet effective power-law model that reconstructs layer-specific rate profiles using only a few discrete production logging tool (PLT) measurements, making the overall approach feasible for real-world applications. The method was implemented and validated on three simulated oil reservoir scenarios, each exhibiting different reservoir/boundary characteristics. The results show that the estimated rate profiles, when input into a stable deconvolution algorithm, produce layer-specific pressure responses. The technique successfully distinguishes the flow behavior and boundary conditions of each individual layer, even under complex conditions such as heterogeneity, no-flow boundaries, dual-porosity formations, and wellbore crosflow during shut-in periods. This advancement enables efficient reservoir description and production optimization, by eliminating the need for mechanical isolation or extended shut-in periods, offering a practical alternative that is aligned with the industry demands for minimum well intervention and cost-effective surveillance.

Multilayer reservoir  /  Commingled production  /  Deconvolution  /  Pressure transient analysis  /  Reservoir characterization
Mina S. Khalaf. Pressure transient analysis of commingled multilayer reservoirs: A deconvolution-based approach for individual layer characterization using sparse PLT data[J]. Petroleum Research, 2026 , 11 (2) : 574 -589 . DOI: 10.1016/j.ptlrs.2025.11.006
Year 2026 volume 11 Issue 2
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doi: 10.1016/j.ptlrs.2025.11.006
  • Receive Date:2025-04-29
  • Online Date:2026-07-29
  • Published:2026-06-10
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  • Received:2025-04-29
  • Revised:2025-11-09
  • Accepted:2025-11-23
Affiliations
    Cairo University, Egypt
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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