Petroleum Engineering – Production
Behzad Orangii; Mohammad Ali Riahi
Abstract
This paper investigates the role of the adequate thickness of the Asmari reservoir formation zones on oil production in one of the Iranian carbonate oil fields. Adequate thickness is a term that includes the total gross thickness of rocks by lithofacies for a selected wellbore. The lithology of the Asmari ...
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This paper investigates the role of the adequate thickness of the Asmari reservoir formation zones on oil production in one of the Iranian carbonate oil fields. Adequate thickness is a term that includes the total gross thickness of rocks by lithofacies for a selected wellbore. The lithology of the Asmari formation in the studied area consists of dolomite, sandstone, lime, dolomitic-lime, sandstone-shale, and shale limestone dolomites. Based on the existing well-logs, the average shale volume, the effective arithmetic means of porosity in the gross intervals, and average water saturation or hydrocarbon-bearing increments of the studied field are calculated using well-logs. In wellbore #A, a depth interval of 2214 to 2296 m shows 9.6% average shale volume, 27.2% average water saturation, and 20.9% average porosity. A depth interval of 2213 to 2280 m, in wellbore #B, shows 6% average shale volume, 21.25% average water saturation, and 28.5% average porosity. Based on our petrophysical assessments, we divide the Asmari reservoir in the studied field into eight zones. Zone 1 is made of carbonate (calcareous and dolomitic), and zones 2–5 are mainly sandstone; zones 7 and 8 are calcareous and shale, and zone 6 is a mixture of all the rocks mentioned above. Among these eight zones, there are two primary hydrocarbon productive zones. The numerical calculation of in situ oil volume showed that zone 2 contains 65% of oil volume in this reservoir. With more than 80% sand, this zone has the highest net hydrocarbon column.
Petroleum Engineering – Exploration
Ziba Hosseini; Sajjad Gharechelou; Asadollah Mahboubi; Reza Moussavi-Harami; Ali Kadkhodaie-Ilkhchi; Mohsen Zeinali
Abstract
The conjugation of two or more Artificial Intelligent (AI) models used to design a single model that has increased in popularity over the recent years for exploration of hydrocarbon reservoirs. In this research, we have successfully predicted shear wave velocity (Vs) with higher accuracy through the ...
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The conjugation of two or more Artificial Intelligent (AI) models used to design a single model that has increased in popularity over the recent years for exploration of hydrocarbon reservoirs. In this research, we have successfully predicted shear wave velocity (Vs) with higher accuracy through the integration of statistical and AI models using petrophysical data in a mixed carbonate-siliciclastic heterogeneous reservoir. In the designed code for multi-model, first Multivariate Linear Regression (MLR) is used to select the more relevant input variables from petrophysical data using weight coefficients of a suggested function. The most influential petrophysical data (Vp, NPHI, RHOB) are passed to Ant colony optimization (ACOR) for training and establishing initial connection weights and biases of back propagation (BP) algorithm. Afterward, BP training algorithm is applied for final weights and acceptable prediction of shear wave velocity. This novel methodology is illustrated by using a case study from the mixed carbonate-siliciclastic reservoir from one of the Iranian oilfields. Results show that the proposed integrated modeling can sufficiently improve the performance of Vs estimation, and is a method applicable to mixed heterogeneous intervals with complicated diagenetic overprints. Furthermore, predicted Vs from this model is well correlated with lithology, facies and diagenesis variations in the formation. Meanwhile, the developed AI multi-model can serve as an effective approach for estimation of rock elastic properties. More accurate prediction of rock elastic properties in several wells could reduce uncertainty of exploration and save plenty of time and cost for oil industries.
