The Prediction of Bubble-point Pressure and Bubble-point Oil Formation Volume Factor in the Absence of PVT Analysis

Elmabrouk, Saber Kh.; Zekri, Abdulrazag Y.; Shirif, Ezeddin

doi:10.1080/10916466.2011.569811

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…In undersaturated reservoirs, the pore space in the reservoir system is occupied by oil and connate water for the model under study for reserve estimation. 52 It can be seen from Figure 9 that as average reservoir pressure depletes with time, connate water from the base case of 20% increased to 40% and 50%. This can potentially lead to early water encroachment as a result of the connate water expansion.…”

Section: Sensitivity Impact Of Water Saturation On Average Reservoir Pressurementioning

confidence: 94%

Computer modeling and simulation for undersaturated primary drive recovery mechanism

Gyan

Xie

Brantson

et al. 2019

Advances in Mechanical Engineering

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The focus of this article is to test the capability of a three-dimensional undersaturated numerical reservoir simulator developed for prediction of actual field performance cases. The simulator development employs unconditional stable Crank-Nicolson second order in time approximation discretization scheme and preconditioned conjugate gradient iterative solver for the derived fluid flow equation. Furthermore, formation volume factor as a function of cell pressure is computed at each time step until bubble-point pressure is reached. The reservoir model is then coupled with a horizontal wellbore model in the simulator by placing a well optimally at the center of its drainage volume. The numerical simulation results show that the numerical solutions improve significantly with the addition of grid blocks. Graphical visualization surface and contour map plots show rapid and continuous average reservoir pressure depletion with time for an earliest possible determination and characterization of primary drive mechanisms. The rate of pressure depletion and recovery factor estimation (1.52%) are the two main characteristics used to identify depletion drive or rock and liquid expansion drive as the best-fit prospective drive for the reservoir model. The total cumulative production of 170,000 stock-tank barrels (MSTB) recovered in 1390 days is in agreement with the field data used. Sensitivity analyses with the numerical simulator illustrated the key impact of reservoir parameters on pressure depletion with less computational expensiveness. Finally, material balance incremental (0.995) and cumulative (1.005) error checks validate the simulator robustness within an acceptable tolerance limit.

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Section: Sensitivity Impact Of Water Saturation On Average Reservoir Pressurementioning

confidence: 94%

Computer modeling and simulation for undersaturated primary drive recovery mechanism

Gyan

Xie

Brantson

et al. 2019

Advances in Mechanical Engineering

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“…Several authors including Elmabrouk et al (2014) and Elshafei and Awady (2013) proposed a black box type of AI models. In all these papers, authors only mentioned the approach they have used to train their models.…”

Section: Artificial Intelligencementioning

confidence: 99%

Intelligent prediction of optimum separation parameters in the multistage crude oil production facilities

Mahmoud

Tariq

Kamal

et al. 2019

J Petrol Explor Prod Technol

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To obtain the high-quality crude oil from the surface processing plants, oil and gas separation plants parameters need to be optimized, by minimizing the intermediate components, flash from the crude oil during primary and secondary separation processes. The aim of this paper is to present an accurate methodology for predicting optimized separation parameters in the multistage crude oil production unit. The new proposed methodology determines the optimum pressures of separators in different stages of separation and consequently optimizes the operating conditions. A dynamic simulator is used to generate the data set for a designed production facility. Then, an optimization algorithm is used to build an optimum artificial neural network model to predict the optimum operating conditions that will maximize the liquid recovery. The ultimate objective of this work is to have an advisory system for optimizing liquid recovery from the production facilities.

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“…Furthermore, sampling for experiments is limited to early producing life of reservoir which imposes a restraint to laboratory measurements (Dake 1988). Early attempts of researchers for presenting a practical, cheap and accurate way of determining FVF from available PVT data led to several empirical correlations (Katz 1942, Knopp and Ramsey 1960, Vazquez and Beggs 1970, Glaso 1980, Al-Marhoun 1988, Farshad et al 1996, Petrosky and Farshad 1993, Omar and Todd 1993, Almehaideb 1997, Al-Shammasi 1999, Dindoruk and Christman 2001, El-Banbi et al 2006, Hemmati and Kharrat 2007, Elmabrouk et al 2010. A striving competition between intelligent systems and empirical correlations versus exactness and generalization has been done to show superiority of intelligent systems (Asoodeh and Kazemi 2013, Asoodeh and Bagheripour 2012a, 2013aAsoodeh 2013, 2014;Gholami et al 2014a, b;Afshar et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Oil Formation Volume Factor Determination Through a Fused Intelligence

Gholami

2016

Acta Geophys.

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Volume change of oil between reservoir condition and standard surface condition is called oil formation volume factor (FVF), which is very time, cost and labor intensive to determine. This study proposes an accurate, rapid and cost-effective approach for determining FVF from reservoir temperature, dissolved gas oil ratio, and specific gravity of both oil and dissolved gas. Firstly, structural risk minimization (SRM) principle of support vector regression (SVR) was employed to construct a robust model for estimating FVF from the aforementioned inputs. Subsequently, an alternating conditional expectation (ACE) was used for approximating optimal transformations of input/output data to a higher correlated data and consequently developing a sophisticated model between transformed data. Eventually, a committee machine with SVR and ACE was constructed through the use of hybrid genetic algorithm-pattern search (GA-PS). Committee machine integrates ACE and SVR models in an optimal linear combination such that makes benefit of both methods. A group of 342 data points was used for model development and a group of 219 data points was used for blind testing the constructed model. Results indicated that the committee machine performed better than individual models.

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The Prediction of Bubble-point Pressure and Bubble-point Oil Formation Volume Factor in the Absence of PVT Analysis

Cited by 8 publications

References 12 publications

Computer modeling and simulation for undersaturated primary drive recovery mechanism

Computer modeling and simulation for undersaturated primary drive recovery mechanism

Intelligent prediction of optimum separation parameters in the multistage crude oil production facilities

Oil Formation Volume Factor Determination Through a Fused Intelligence

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