Box-Behnken design for assessment proxy model of miscible CO 2 -WAG in heterogeneous clastic reservoir

Jaber, Ahmed Khalil; Awang, Mokhtar; Lenn, Chris

doi:10.1016/j.jngse.2017.02.020

Cited by 22 publications

(14 citation statements)

References 9 publications

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“…It was found practically to be the critical factor controlling the WAG process design (Behrouz et al 2007). Wu et al (2004) concluded in their study that the cycle length during miscible WAG flooding was found to be a critical factor in the WAG process design in the heterogeneous reservoir (Jaber et al 2017).…”

Section: Wag Injection Schemementioning

confidence: 99%

See 1 more Smart Citation

Novel approach for predicting water alternating gas injection recovery factor

Belazreg

Mahmood

Aulia

2019

J Petrol Explor Prod Technol

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Water alternating gas (WAG) injection process is a proven EOR technology that has been successfully deployed in many fields around the globe. The performance of WAG process is measured by its incremental recovery factor over secondary recovery. The application of this technology remains limited due to the complexity of the WAG injection process which requires time-consuming in-depth technical studies. This research was performed for a purpose of developing a predictive model for WAG incremental recovery factor based on integrated approach that involves reservoir simulation and data mining. A thousand reservoir simulation models were developed to evaluate WAG injection performance over waterflooding. Reservoir model parameters assessed in this research study were horizontal and vertical permeabilities, fluids properties, WAG injection scheme, fluids mobility, trapped gas saturation, reservoir pressure, residual oil saturation to gas, and injected gas volume. The outcome of the WAG simulation models was fed to the two selected data mining techniques, regression and group method of data handling (GMDH), to build WAG incremental recovery factor predictive model. Input data to the machine learning technique were split into two sets: 70% for training the model and 30% for model validation. Predictive models that calculate WAG incremental recovery factor as a function of the input parameters were developed. The predictive models correlation coefficient of 0.766 and 0.853 and root mean square error of 3.571 and 2.893 were achieved from regression and GMDH methods, respectively. GMDH technique demonstrated its strength and ability in selecting effective predictors, optimizing network structure, and achieving more accurate predictive model. The achieved WAG incremental recovery factor predictive models are expected to help reservoir engineers perform quick evaluation of WAG performance and assess a WAG project risk prior launching detailed time-consuming and costly technical studies.

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Section: Wag Injection Schemementioning

confidence: 99%

“…In recent years, water alternating gas (WAG) flooding process has gained an increasing interest in enhancing oil recovery worldwide. Additional economic profit from WAG process is the reduction of the required amount of gas to be injected into the reservoir (Jaber et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Novel approach for predicting water alternating gas injection recovery factor

Belazreg

Mahmood

Aulia

2019

J Petrol Explor Prod Technol

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“…In high permeability core, due to higher pressure and suppression of gas channel by the way of CO2-WAG, the injected fluid can enter or dissolve in the crude oil in the smaller pores, increasing the sweep volume and oil displacement efficiency in the cores. In addition, a part of the fluid enters and the medium and low permeability cores under a higher differential pressure, the oil displacement effect of the injected fluid on these cores is also improved [13,18] . In general, CO2-WAG flooding not only improves the oil displacement effect of each layer, especially the high permeability layer, but also weakens the impact of multilayer system heterogeneity on the overall system oil recovery performance.…”

Section: Residual Oil Distribution and Oil Recoverymentioning

confidence: 99%

“…Flooding with both CO2 and CO2-WAG are common EOR strategies which have been widely used in a significant number of oilfields [13][14][15] . Each flooding scheme has individual characteristics in terms of displacement effect, injection difficulty, and impact on the reservoir's physical properties [16][17][18][19] . Since CO2 has a lower viscosity and density at reservoir conditions, the pressure required for CO2 injection during CO2 flooding is small.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Oil Recovery Performance and Permeability Damage in Multilayer Reservoirs after CO₂ and Water–Alternating-CO₂ (CO₂–WAG) Flooding at Miscible Pressures

et al. 2019

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Blockage in reservoirs caused by asphaltene deposits and inorganic interactions is a serious problem that may exacerbate the complexity of displacement characteristics in heterogeneous multilayer sandstone reservoirs and affect crude oil recovery performance during CO2 and CO2-WAG flooding. In this study, experiments of both CO2 and CO2-WAG flooding were carried out on the same multilayer systems under miscible conditions (70℃, 18 MPa). The two flooding methods were evaluated for oil production performance and reservoir damage. The experimental results indicate that, after CO2 flooding, the entire system has a low oil recovery factor (RF) of 27.6%, and oil is produced mainly from the high permeability layer (91.4%), whilst the residual oil remains predominantly in the medium and low permeability layers. The injection pressure of CO2-WAG flooding is high, but the timing of CO2 breakthrough (BT) is late, and the oil RF of the entire system reaches 44.5%. The contribution rate of oil production in medium and low permeability layers is improved to 3.8% and 17.1%, respectively. Furthermore, the permeability of the high permeability layer decreases by 16.8% after CO2 flooding, which is mainly due to asphaltene precipitation. However, after CO2-WAG flooding, the permeability of each layer is significantly reduced, namely by 29.4%, 16.8% and 6.9% respectively. Asphaltene precipitation is still the main factor, but permeability decline caused by CO2-brine-rock interactions cannot be ignored, especially in the high permeability layer (6.1%). Therefore, for multilayer reservoirs with high heterogeneity, CO2-WAG flooding provides the better oil displacement performance, but prevention and control measures for asphaltene precipitation are more necessary.

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“…To construct a proxy (or surrogate) model, supervised learning is carried out using reservoir parameters in the input layer and production responses in the output layer. This approach has primarily been studied as a replacement for compositional and unconventional simulations which require significant simulation time [16][17][18][19][20]. Inverse models reverse the order of the parameters: the production responses and reservoir parameters for the input and output layers, respectively.…”

Section: Introductionmentioning

confidence: 99%

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Kang

Lee

2020

Geofluids

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Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model’s TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully.

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Box-Behnken design for assessment proxy model of miscible CO 2 -WAG in heterogeneous clastic reservoir

Cited by 22 publications

References 9 publications

Novel approach for predicting water alternating gas injection recovery factor

Novel approach for predicting water alternating gas injection recovery factor

Experimental Investigation of Oil Recovery Performance and Permeability Damage in Multilayer Reservoirs after CO₂ and Water–Alternating-CO₂ (CO₂–WAG) Flooding at Miscible Pressures

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

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Box-Behnken design for assessment proxy model of miscible CO 2 -WAG in heterogeneous clastic reservoir

Cited by 22 publications

References 9 publications

Novel approach for predicting water alternating gas injection recovery factor

Novel approach for predicting water alternating gas injection recovery factor

Experimental Investigation of Oil Recovery Performance and Permeability Damage in Multilayer Reservoirs after CO2 and Water–Alternating-CO2 (CO2–WAG) Flooding at Miscible Pressures

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

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Experimental Investigation of Oil Recovery Performance and Permeability Damage in Multilayer Reservoirs after CO₂ and Water–Alternating-CO₂ (CO₂–WAG) Flooding at Miscible Pressures