Efficient Reservoir Modeling-Statistical Approach for Evaluation and Development of Future Waterdrive Undersaturated Oil Reservoir Performance

Al-Mudhafer, Wathiq Jassim Mohammed; Al-Khazraji, Ahmed Jaber

doi:10.2523/iptc-18102-ms

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Singhal et al economic analysis (net present value, mobile saturation calculation, the ideal number of extra wells drilled and their locations, compared with primary, secondary, and EOR processes) is used to direct the activities of a reservoir modeling group to determine the best plan to optimize recovery (which could be waterflooding, gas injection or WAG). Using this method significantly reduced both the level of uncertainty and the number of sensitivity analyses that were needed [24].…”

Section: Fig 2 Integration Between Optimization and Reservoir Simulationmentioning

confidence: 99%

Comparison Different Techniques of Optimum Location for Infill Well Drilling

Al-adliy,

Al-mahdawi,

Mohammed

et al. 2024

IJCPE

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Increasing oil production from a reservoir can be achieved by decreasing the distance between the injector and the producer through a process known as infill drilling, which involves a pattern water flood. The main objective of this study is to provide a comprehensive overview of the optimal infill well location and the research and applications available to enhance the oil recovery factor, leading to increased economic profits. one effective empirical approach used in this study is based on decline curve analysis, which analyzes the production history of the well to determine the final economic recovery. Additionally, a numerical method that combines numerical simulation and optimization techniques has been proven to be successful in determining optimal infill drilling locations. The research results show that the volumetric computation of oil in place is a useful method for estimating the number of infill wells needed, but it does not consider heterogeneity and continuity. On the other hand, the numerical simulation and optimization techniques can quantify the remaining mobile oil post-infill drilling and establish optimal pattern configurations for maximum recovery at their centers.

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Section: Fig 2 Integration Between Optimization and Reservoir Simulationmentioning

confidence: 99%

Comparison Different Techniques of Optimum Location for Infill Well Drilling

Al-adliy,

Al-mahdawi,

Mohammed

et al. 2024

IJCPE

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Bayesian Identifying of Influential Factors Affecting Steamflooding Processes Performance in Heterogeneous Bitumen-Oil Sandstone Reservoir

Al‐Mudhafar

Klimenko

2015

All Days

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The Steamflooding was considered in this research to extract discontinuous bitumen layers and improve oil recovery in the Upper Sandstone Member of South Rumaila Oil Field, located in Southern Iraq. The reservoir heterogeneity and bitumen layers, which are located at the water-oil contacts, impede water approaching into the reservoir from an infinite active aquifer; therefore, Steamflooding came over this situation by efficiently extracting the bitumen. This research focused on using a Bayesian framework of Experimental design with thermodynamic reservoir flow modeling for the purpose of identifying the most influential factors that impact the reservoir performance through Steamflooding processes. Thermodynamic reservoir simulation was used to compute flow response, cumulative oil production, to evaluate the various what-if scenarios that are created by experimental design. Latin Hypercube Sampling was adopted for its discrepancy and uniformity to generate many experimental runs that capture the variety of Steamflooding process evaluation.The factors that were considered to test reservoir response are steam injection pressure, steam quality, steam injection rate, steam temperature, maximum oil production rate, minimum bottom hole pressure, maximum water cut, skin factor. Additionally, the reservoir properties of horizontal permeability, porosity, anisotropy ratio, aquifer radius, and rock compressibility are included. In order to find out the most influential factors on the Steamflooding performance, Bayesian Model Averaging (BMA) was adopted as model selection in the experimental design in comparison with the conventional stepwise linear regression. Model selection process in BMA considers the model's posterior probability and Bayesian Information Criterion (BIC). BMA produces a posterior distribution of the outcome factor that represents the weighted average of the posterior distributions of that factor for each likely model. Among multi models, few ones were selected that their posterior probabilities sum equals one. The best model selection has the maximum posterior probability and minimum Bayesian Information Criterion; nevertheless, the reduced model is determined when the non-zero probability of coefficient for a given predictor equals more than 50% for all the sampled models.All these variables are shown in the computed Occam's window. The results showed that BMA algorithm has efficiently eliminate the non-influential factors on the Steamflooding performance. The most influential factors on the Steamflooding performance were identified by LHS are maximum oil production rate, maximum water cut, in addition to permeability, porosity, anisotropy ratio and rock compressibility. However, the conventional stepwise regression has shown that porosity is the most influential factor with limited effect for all other factors that is physically meaningless.

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Lessons Learned from IOR Steamflooding in a Bitumen-Light Oil Heterogeneous Reservoir

Mudhafar¹,

Nasab²

2015

Proceedings

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SUMMARYThe Steamflooding was considered in this research to extract the discontinuous bitumen layers that are located at the oil-water contact for the heterogeneous light oil sandstone reservoir of South Rumaila Field. The reservoir heterogeneity and the bitumen layers impede water aquifer approaching into the reservoir; therefore, Steamflooding would be efficient to extract bitumen layers and improve oil recovery. This research focused on adopting three Design of Experiments (DoE) approaches with thermodynamic reservoir flow simulation to identify the most influential factors that impact the reservoir performance through Steamflooding process. Meanwhile, the thermodynamic simulation was used to evaluate the various what-if scenarios and compute cumulative oil production that was considered as a response in the experimental design procedure. In this paper, full factorial design (FFD) and orthogonal arrays design (OAD) were adopted along with Hammersley Sequence Sampling (HSS) for that purpose. HSS is a low discrepancy and uniform space filling decimal points sampling that provide multiple levels for each factor. The factors are steam injection pressure, steam quality, steam injection rate, steam temperature, and number of injectors. To validate the overall design and each factor, analysis of variance (ANOVA) test was used to assess the influential role for each factor. In comparison with no-injection base case, Steamflooding has proved its feasibility to extract bitumen and improve recovery factor that reached to 80.018% by the end of 12 years prediction period; nevertheless, oil recovery for the base case was only 68.231 %, which is equal to the value with Steamflooding only after 11 months when the Steam injection starts. The linear DoE model of HSS has shown its validity to handle wide variety experiments of the problem. The main influential factors that were identified by DoE models are steam quality, steam injection rate and some of the interaction terms that include other factors.

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Efficient Reservoir Modeling-Statistical Approach for Evaluation and Development of Future Waterdrive Undersaturated Oil Reservoir Performance

Cited by 3 publications

References 14 publications

Comparison Different Techniques of Optimum Location for Infill Well Drilling

Comparison Different Techniques of Optimum Location for Infill Well Drilling

Bayesian Identifying of Influential Factors Affecting Steamflooding Processes Performance in Heterogeneous Bitumen-Oil Sandstone Reservoir

Lessons Learned from IOR Steamflooding in a Bitumen-Light Oil Heterogeneous Reservoir

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