RESERVOIR SIMULATION FOR RESERVOIR MANAGEMENT

1986 ◽  
Vol 26 (1) ◽  
pp. 397
Author(s):  
A.B. Kaliszewski
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
History Matching ◽  
Reservoir Management ◽  
Primary Objective ◽  
Geological Mapping ◽  
Management Tool ◽  
The Subject ◽  
Simulation Results ◽  
Very High

The Hutton reservoir in the Merrimelia Field (Cooper-Eromanga Basin) was the subject of a 3-D reservoir simulation study. The primary objective of the study was to develop a reservoir management tool for evaluating the performance of the field under various depletion options.The study confirmed that the ultimate oil recovery from this strong water drive reservoir was not adversely affected by increasing total fluid offtake rate. However, any decisions regarding changes to the depletion scheme such as increasing production rates, if based solely on computer simulation results, should be viewed with caution. Careful monitoring of any changes to the depletion philosophy and checking of actual data against simulation predictions are essential to ensure that oil production rate and ultimate recovery are optimised.The model assisted in evaluating the economics of development drilling. While the simulation results are dependent on the validity of geological mapping, the model was useful in confirming that, due to very high transmissibility in the Hutton reservoir, additional wells would only accelerate production rather than increase ultimate recovery. The issue of drilling wells thus became one of balancing the benefits of accelerating production against the geological risk associated with that well.Interaction between the reservoir engineer and various disciplines, particularly development geology, is critical in the development and application of a good working simulation model. This was found to be especially important during the history matching phase in the study. If engineers and development geologists can learn more of the others' discipline and appreciate the role that each has to play in simulation studies, the validity of such models can only be improved.The paper addresses a number of the pitfalls commonly encountered in application of reservoir simulation results.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Effect of Solvent Concentration on Scaling Butane Solvent Enhanced Oil Recovery Processes Using Reservoir Simulation

2020 ◽  
Vol 27 ◽  
pp. 136-165
Author(s):  
Shelley Lorimer ◽  
Govind Kumar ◽  
Sherif Abdelkareem
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
Gravity Drainage ◽  
Thermal Processes ◽  
Field Scale ◽  
Solvent Concentration ◽  
Capillary Effects ◽  
Recovery Curves ◽  
Recovery Processes ◽  
Simulation Results

Understanding scaling of enhanced oil/bitumen recovery processes is essential in moving laboratory scale experimental results to field scale. Scaling theory for thermal processes is well understood and has been applied to steam processes. However, scaling of hybrid steam (thermal) /solvent (mass transfer) processes is still not well defined nor well understood. This paper investigates the scaling behavior of hybrid steam/butane gravity drainage processes using reservoir simulation (commercial thermal compositional simulator CMG STARSTM). Previous research has used reservoir simulation to confirm scaling groups for waterflooding. A similar strategy was used in this study whereby the scaling of a hybrid (steam) solvent oil recovery process was examined using reservoir simulations at three different reservoir scales: lab scale, semi-field scale and field scale to examine the influence of the mass transfer mechanisms of diffusion and dispersion on the scalability of the process. In particular, the influence of butane solvent concentration on scaling a steam/butane gravity drainage process was investigated by considering several butane mole fraction concentrations injected with steam (1%, 2%, 5%, 7%, 10%, 15%, 21%, 25% and 50%). Temperature contours, and mole fraction contours of butane in both the oil and gas phases were examined for various solvent injection concentrations to examine scalability. Numerical results are provided with no diffusion and dispersion, diffusion only, dispersion only and with both diffusion and dispersion added to the simulations. Results confirmed scalability of the process with no capillary effects when the simulation results were non-dimensionalized, although there were some issues with material balance errors in some of the simulation results particularly at high solvent concentrations. For low injection concentrations, the contours were almost identical (indicating scalability) for the three scales for the operating condition studied. In addition, capillary effects were also studied, and similar to scaling thermal processes, the capillarity effects influenced scalability of the process under the conditions studied particularly at higher injection concentrations. Scalability using reservoir simulation was generally preserved with low injection concentrations, but unusual behavior was observed at higher injection concentrations (>5%). Oil recovery curves were non-dimensionalized to make comparisons amongst the three scales. The oil recovery curves displayed an unusual S-shaped behavior at higher injection concentrations when capillary effects were included especially for the lab and semi-field scales. In all cases when all of the mechanisms are included (diffusion, dispersion and capillary effects), Scale 1 shows a much faster recovery than Scale 3 which suggests that the lab scale might temporally overestimate the field scale recovery for this particular process scenario.

History Matching and Performance Prediction of a Polymer Flood Pilot in Heavy Oil Reservoir on Alaska North Slope

2021 ◽  
Author(s):  
Xindan Wang ◽  
Cody Keith ◽  
Yin Zhang ◽  
Abhijit Dandekar ◽  
Samson Ning ◽  
...  
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
Reservoir Simulation ◽  
Heavy Oil ◽  
History Matching ◽  
Polymer Flooding ◽  
North Slope ◽  
Polymer Flood ◽  
Reservoir Simulation Model ◽  
Water Cut

Abstract The first-ever polymer flood pilot to enhance heavy oil recovery on Alaska North Slope (ANS) is ongoing. After more than 2.5 years of polymer injection, significant benefit has been observed from the decrease in water cut from 65% to less than 15% in the project producers. The primary objective of this study is to develop a robust history-matched reservoir simulation model capable of predicting future polymer flood performance. In this work, the reservoir simulation model has been developed based on the geological model and available reservoir and fluid data. In particular, four high transmissibility strips were introduced to connect the injector-producer well pairs, simulating short-circuiting flow behavior that can be explained by viscous fingering and reproducing the water cut history. The strip transmissibilities were manually tuned to improve the history matching results during the waterflooding and polymer flooding periods, respectively. It has been found that higher strip transmissibilities match the sharp water cut increase very well in the waterflooding period. Then the strip transmissibilities need to be reduced with time to match the significant water cut reduction. The viscous fingering effect in the reservoir during waterflooding and the restoration of injection conformance during polymer flooding have been effectively represented. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the oil recovery performance under different development strategies, with consideration for sensitivity to polymer parameter uncertainties. The oil recovery factor with polymer flooding can reach about 39% in 30 years, twice as much as forecasted with continued waterflooding. Besides, the updated reservoir model has been successfully employed to forecast polymer utilization, a valuable parameter to evaluate the pilot test’s economic efficiency. All the investigated development strategies indicate polymer utilization lower than 3.5 lbs/bbl in 30 years, which is economically attractive.

التغير المناخي واثره في المقنن المائي لمحصول العنب في محافظة ديالى

2019 ◽  
Vol 1 (34) ◽  
pp. 391-422
Author(s):  
اشواق حسن حميد صالح
Keyword(s):  
Climate Change ◽  
Statistical Analysis ◽  
Wind Speed ◽  
Extreme Temperature ◽  
Solar Brightness ◽  
The Subject ◽  
Climate Station ◽  
The Times ◽  
High Level ◽  
Very High

Climate change and its impact on water resources is the problem of the times. Therefore, this study is concerned with the subject of climate change and its impact on the water ration of the grape harvest in Diyala Governorate. The study was based on the data of the Khanaqin climate station for the period 1973-2017, (1986-2017) due to lack of data at governorate level. The general trend of the elements of the climate and its effect on the water formula was extracted. The equation of change was extracted for the duration of the study. The statistical analysis was also used between the elements of the climate (actual brightness, normal temperature, micro and maximum degrees Celsius, wind speed m / s, relative humidity% The results of the statistical analysis confirm that the water ration for the study area is based mainly on the X7 evaporation / netting variable, which is affected by a set of independent variables X1 Solar Brightness X4 X5 Extreme Temperature Wind Speed ​​3X Minimal Temperature and Very High Level .

Relationship Analysis of Islamic Banking between Malaysia and Pakistan

2018 ◽  
Vol 5 (4) ◽  
pp. 1-6
Author(s):  
Khurram Faisal Jamal
Keyword(s):  
Large Range ◽  
Primary Objective ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Relationship Analysis ◽  
Banking Institutions ◽  
Qualitative Technique ◽  
Performance Problem ◽  
Very High ◽  
Product Preference

Islamic banking is basically a system of financial intermediation, its primary objective is to avoid receipt and payment of interest. Islam does not only prohibit dealing with interest but also with liquor, pork, gambling, pornography and any other thing which are considered haram according to Shariah. The objectives of the research is to study and describe the Islamic financing techniques used by Islamic banking institutions in Malaysia and Pakistan. For this research seven variables Promotion, Product, Preference, Knowledge, Performance, Problem and Infrastructure was taken. Qualitative technique was used to answer the research objective. The findings of research indicate that lack of awareness of Islamic banking is very high in Pakistan as compared to Malaysia. A few promotions were used by Islamic banks in Pakistan while in Malaysia customers are knowledgeable about Islamic banking because banks promote them aggressively. There is a need of government and education sector support to promote Islamic banking in both countries. The study also found that Islamic banks in Malaysia have large range of products as compared to Pakistan. The practitioners from both countries are agreed at this point that BBA, Ijarah and Murabaha are more profitable and less risky than Musharaka and Mudaraba. The Islamic banking products are almost used for same purposes in both countries while some differences are also exists.  Keywords: Islamic Finance, Comparative Study, Malaysia, Pakistan

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

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