Reservoir Flow Simulation Study for a Sector in Main Pay-South Rumaila Oil Field

Mohammed, Wathiq Jassim; Al-Jawad, Mohammed S.; Al-Shamaa, Dunia Abdulshaib Abdu

doi:10.2118/126427-ms

Cited by 30 publications

(10 citation statements)

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“…Zubair formation is composed on five reservoirs based on sand-to-shale ratio: upper shale (top), upper sandstone, middle shale, lower sand, and lower shale (bottom). The upper sandstone reservoir in Zubair formation represents the main pay of South Rumaila Field (Mohammed et al 2010). The main pay comprises of five dominated sandstone units (AB, DJ1, DJ2, LN1, and LN2), separated by four discontinuous shale layers (C1, C2, K1, and K2) located between the five units (Al-Mudhafar 2017).…”

Section: Field and Reservoir Descriptionmentioning

confidence: 99%

“…Moreover, it precludes the instability of the compositional transition between the injected CO 2 and crude oil (viscous fingering). Contrary, selecting coarse grid blocks in the compositional modeling of the CO 2 flooding Mohammed et al (2010) gives rise to a significant effect of numerical dispersion, which leads to inaccurate recovery calculations.…”

Section: Gagd Process Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Geological and production uncertainty assessments of the cyclic $$\text{CO}_2$$ CO 2 -assisted gravity drainage EOR process: a case study from South Rumaila oil field

Al‐Mudhafar

Rao

Srinivasan

2018

J Petrol Explor Prod Technol

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Risk or uncertainty assessment in the reservoir flow modeling, especially in real field-scale evaluation, is essential to make a trustful decision regarding the future development plans. This paper presents an efficient uncertainty assessment workflow of geological and production data through the cyclic CO 2-assisted gravity drainage (GAGD) process in South Rumaila oil field in Southern Iraq. First, the sequential Gaussian simulation created a large number of reservoir stochastic realizations that capture the entire geological uncertainty space. Second, ranking was applied to select the quartiles (P10, P20, … , P90) of reservoir permeability and anisotropy ratio to quantify the geological uncertainty. Next, the equation of state-compositional flow model was constructed to evaluate these realizations by calculating the reservoir flow response. Then, 81 designed simulations were created by factorial design considering the combined realizations of permeability and anisotropy ratio. In a successive step, the most-likely model was considered for the uncertainty quantification of the operational decision parameters through the cyclic GAGD process to restrict the uncertainty space, which leads to obtain the true optimal scenario. The cyclic GAGD operational parameters include durations of injection, soaking, and production and the minimum bottom hole pressure in production wells. The compositional reservoir flow model was again used to evaluate the multiple simulations created by the proxy-based Box-Behnken design and Monte Carlo simulation. The combined geological and production uncertainty workflow gave an idea about the uncertainty or risk space of the predicted reservoir flow response in the future cyclic GAGD process performance.

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Section: Field and Reservoir Descriptionmentioning

confidence: 99%

Section: Gagd Process Simulationmentioning

confidence: 99%

Geological and production uncertainty assessments of the cyclic $$\text{CO}_2$$ CO 2 -assisted gravity drainage EOR process: a case study from South Rumaila oil field

Al‐Mudhafar

Rao

Srinivasan

2018

J Petrol Explor Prod Technol

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“…Several techniques are used to create a proper approximation of the shared boundary conditions. A widespread procedure is to select the sectors with an irrelevant or even no flux contour and implement a Dirichlet Boundary condition with fixed flux equal to 0 or another constant [1,5,14,16,20,23]. This type of solution is applied to reservoirs with low interaction among wells, when operational changes do not significantly impact the track of streamlines or when the objective is only to simulate a short period of time [2,15].…”

Section: Introductionmentioning

confidence: 99%

“…Some commercial simulators already offer implemented functionalities to directly select a flux or a pressure to be applied on the boundary, but it is still uncommon in reservoir simulation studies. Virtual wells [6,22], virtual aquifer [16] and tank cell volume (Bruijnzeels and O' Halloran) [7] are common elements used to impose a specific contour condition and mimic the flux among sectors. In general, the problem is to understand the impact of each parameter of the methodology in the boundary flux and try to set the most suitable parameters that define the element.…”

Section: Introductionmentioning

confidence: 99%

Sector modeling using iterative boundary pressure estimation

Barreto

Avansi

Schiozer

2021

J Braz. Soc. Mech. Sci. Eng.

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Simulation of large-scale fields with high-level of heterogeneity are commonly modeled using high-resolution grid cell, which can be time-consuming to run the entire reservoir numerical model. One way to deal with these cases is to split the model into sectors and individually simulate them using artificial boundary conditions to represent the fluid flow among the sectors. However, the representation of the boundary condition can be affected by operational or strategic changes, leading to poor-quality forecasts. This work aims to present a methodology to update the pressure in the boundary of sector models using virtual wells. The methodology consists of balancing the pressure of adjacent sector models over the simulation run. The update procedure is carried out observing boundary block pressure and applying an estimation of the flow rate between sectors and using a pair of virtual wells (producer/injector) to perform it. The methodology was tested in the Brugge Field Benchmark Case, which is a complex case that presents high-interaction activity among the wells. A tree of tests was created to study the parameters of the method, as a different number of times to update the boundary pressure and a different number of pairs of virtual wells. Furthermore, two different ways to split up the reservoir model were also tested: using streamlines and not considering any dynamic flow information, to evaluate the importance of using a technique to select the sectors. The quality of the results was quantified using the indicator called normalized quadratic deviation with sign, comparing the sector simulation and the entire reservoir model simulation. The results show that the methodology was able to improve the quality of the response to all cases. The case splitting the reservoir using streamlines achieved better results, although the improvement of the response was lower than found for the random split. The number of times to update the model was the parameter with a more significant impact on the quality of the results. A higher number of update times tended to achieve better outcomes. The number of virtual wells was the parameter that caused a lower impact on the results, and significant gains in increasing the number of virtual wells were not observed. Therefore, just a few numbers of virtual wells can balance the boundary pressure. To conclude, the proposed methodology can improve the quality of the results for a sector simulation model. The results also show an improvement when the methodology is combined with a splitting procedure of the reservoir using streamlines. The number of times to update the model can significantly affect the results, but the number of virtual wells had a lower impact.

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“…(Left) Sectors of the field with Well Locations. (Right) Isobaric Map of South Rumaila Oil Field, Modified from(Mohammed et al , 2010) …”

mentioning

confidence: 99%

Advanced ML and AI Approaches for Proxy-based Optimization of CO2-Enhanced Oil Recovery in Heterogeneous Clastic Reservoirs

Al‐Mudhafar¹,

Rao²,

Srinivasan³

2019

Preprint

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The EOS-compositional reservoir simulation, Design of Experiments, and Proxy Modeling were integrated to obtain the optimal future performance scenario and to construct the most accurate simplified model alternative to the complex reservoir flow simulation. This integrated workflow was adopted on a sector of the main pay/upper sandstone member in the South Rumaila oil field, located in Iraq. After conducting the acceptable history matching, 6 operational decision parameters, which constrain the production and injection activities, were optimized for their optimal level to achieve optimal flow response factor. Given these decision parameters, the Latin Hypercube Sampling was employed as a low-discrepancy and uniform approach to create hundreds of simulation runs (experiments) to construct a proxy-based optimization approach. The optimal cumulative oil production, by the end of the prediction period, led to obtaining 4.6039 MMMSTB of oil production, while the base case of the GAGD process evaluation of default parameters’ setting resulted to obtain 4.3887 MMMSTB of oil production.Finally, four proxy metamodels were constructed to provide simplified models alternative to the complex compositional reservoir simulation: Second-Degree Polynomial Equation (QM), Multivariate Additive Regression Splines (MARS), Fuzzy Logic-Genetic Algorithm (FUzzy-GEnetic), and Generalized Boosted Modeling (GBM). The cross-validation with Adjusted $R^{2}_{adj}$ and Root Mean Square Error were employed to find the optimal proxy model that has the least mismatch between the proxy- and simulator-based cumulative oil production response through CO2-GAGD process. It was concluded that both GBM and FUzzy-GEnetic are the most accurate simplified alternative metamodels for the GAGD Process evaluation and prediction.

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Reservoir Flow Simulation Study for a Sector in Main Pay-South Rumaila Oil Field

Cited by 30 publications

References 1 publication

Geological and production uncertainty assessments of the cyclic $$\text{CO}_2$$ CO 2 -assisted gravity drainage EOR process: a case study from South Rumaila oil field

Geological and production uncertainty assessments of the cyclic $$\text{CO}_2$$ CO 2 -assisted gravity drainage EOR process: a case study from South Rumaila oil field

Sector modeling using iterative boundary pressure estimation

Advanced ML and AI Approaches for Proxy-based Optimization of CO2-Enhanced Oil Recovery in Heterogeneous Clastic Reservoirs

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