Spatial Modeling of Hydrocarbon Productivity in the Nahr Umr Formation at the Luhais Oil Field, Southern Iraq
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Original Paper
Spatial Modeling of Hydrocarbon Productivity in the Nahr Umr Formation at the Luhais Oil Field, Southern Iraq Amna M. Handhal,1,2 Amjad A. Hussein,1 Alaa M. Al-Abadi ,1,2,3 and Frank R. Ettensohn1,2,3 Received 29 June 2020; accepted 18 September 2020
In this study, a trial exercise was performed for the first time to model the productivity of a reservoir unit, using a GIS-based hybridization of ShannonÕs entropy method and the technique for order preference by similarity to an ideal solution (TOPSIS) approach. A case study from the middle reservoir unit of Nahr Umr Formation in the Luhais oil field in southern Iraq was used to demonstrate the benefits of the proposed methodology in managing hydrocarbon reservoirs with cost-effective modeling techniques. The heterogeneity of the reservoir unit was firstly quantified using the Lorenz coefficient (Lk) and the Dykstra–Parsons permeability variation (Vk). The average calculated Lk and Vk were 0.65 (heterogeneous) and 0.93 (very heterogeneous), respectively. This stage of the analysis confirmed the heterogeneous nature of the reservoir unit. To overcome the problem reservoir heterogeneity, the hydraulic flow unit (HFU) concept was used. Interactive Petrophysics software was used to create HFUs, and the number of HFUs was optimized using k-means clustering techniques. The estimated number of HFUs was 2. For each HFU, seven petrophysical properties or factors, namely porosity (/), thickness, volume of shale (Vsh), bulk volume of water (BVW), total water saturation (SWT), hydrocarbon saturation (Sh), and bulk volume of hydrocarbons (BVH), were calculated for each well location based on well logs and core data availability. The ordinary kriging technique was used to interpolate the seven petrophysical properties for each HFU over the study area. ShannonÕs entropy model was then used to assign factor weights for each HFU. In the case of HFU-1, the calculated weights were 0.218, 0.190, 0.141, 0.132, 0.111, 0.107, and 0.103 for Sh, unit thickness, BVH, BVW, /, SWT, and Vsh, respectively. For HFU-2, the calculated weights were 0.179, 0.178, 0.170, 0.154, 0.146, 0.092, and 0.081, for Vsh, BVH, Sh, SWT, unit thickness, BVW, and /, respectively. The TOPSIS algorithm was then implemented using R statistical software, and ranked values from the TOPSIS were interpolated using the ordinary kriging technique to reveal the spatial distribution of hydrocarbon productivity after division into three productivity zones: low, moderate, and high. For HFU-1, these zones
1
Department of Earth and Environmental Sciences, University of Kentucky, Lexington, USA. 2 Department of Geology, College of Science, University of Basrah, Basrah, Iraq. 3 To whom correspondence should be addressed; e-mail: [email protected], [email protected], [email protected]
Ó 2020 International Association for Mathematical Geosciences
Handhal, Hussein, Al-Abadi, and Ettensohn encompass 32, 22, and 45 km2 for the low-, moderate-, and high-productivity zones, respectively. For HFU-2, these zone
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