Development of Recovery Factor Model For Water Drive and Depletion Drive Reservoirs in The Niger Delta

Onolemhemhen, Rita U.; Isehunwa, S. O.; Salufu, S. O.

doi:10.2118/184283-ms

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…Using model-based multilinear regression, Oseh and Omotara [28] evaluated the Nigerian delta recovery factor. Similarly, a study was proposed to test the Nigerian Delta recovery factor for water-driven and depletion reservoirs [29]. Their study has some limits due to the complexity of the reservoir data.…”

Section: Related Workmentioning

confidence: 99%

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

Al-Tashi

Akhir

Abdulkadir

et al. 2021

JMSE

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The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.

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Section: Related Workmentioning

confidence: 99%

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

Al-Tashi

Akhir

Abdulkadir

et al. 2021

JMSE

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“…Onolemhemhen et al [30] came up with three models to predict the oil RF for water drive, solution gas drive, and secondary recovery with water injection in the Niger delta. The authors used the data from 136 reservoirs to develop their models.…”

Section: Applications Of Artificial Intelligence In the Petroleum Indmentioning

confidence: 99%

Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

et al. 2019

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Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

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“…where T i 6 1 2 ;j;k ¼ ðb c K x A x =mB o DxÞ i 6 1=2;j;k , T i;j 6 1 2 ;k ¼ ðb c K y A y =mB o DyÞ i;j61=2;k , T i;j;k6 1 2 ¼ðb c K z A z =mB o DzÞ i;j;k61=2 , and G i;j;k ¼ðV b [C t =a c B o Þ i;j;k .…”

Section: Mathematical Fluid Flow Equationunclassified

“…However, one effective hydrocarbon recovery strategy is to take into account the effect of natural reservoir energy. 1 Several reservoir engineers have, over the past, employed strategies to understand the behavior as well as to improve the reservoir recovery factor, thus the ratio of the volume of hydrocarbons produced to the volume of original oil in place (OOIP).…”

Section: Introductionmentioning

confidence: 99%

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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Development of Recovery Factor Model For Water Drive and Depletion Drive Reservoirs in The Niger Delta

Cited by 9 publications

References 6 publications

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

Computer modeling and simulation for undersaturated primary drive recovery mechanism

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