Application of Group Method of Data Handling for Geological Modeling of Vankor Field

Water alternating gas (WAG) injection process is a proven enhanced oil recovery (EOR) technology with many successful field applications around the world. WAG pilot projects demonstrate that WAG incremental recovery factor typically ranges from 5 to 10% of original oil in place, though up to 20% has been observed in some fields. Despite its proven success, WAG application growth has been very slow. One of the reasons is the unavailability of robust analytical predictive tools that could estimate WAG incremental recovery factor, which is required for preliminary economic analysis before committing to expensive and time-consuming detailed technical studies and field pilot test that often requires a lot of input data. A seminumerical model for WAG incremental recovery factor prediction was developed based on data mining of published WAG pilots to fill this gap. An extensive review of published WAG pilot projects was carried out, and consequently, 33 projects from 28 fields around the world were selected for this research study. Field WAG incremental recovery factor and parameters with total of one hundred and seventy-seven (177) observations were inputted to the predictive model. A predictive model was developed using both regression and group method of data handling (GMDH) techniques; 70% of the 33 WAG pilot projects data were used as validation, whereas remaining 30% of data set were used for validation. The predictive model results achieved with coefficient of determination (R 2) from the regression method were ranging from 0.892 to 0.946 and 0.854 to 0.917 for training and validation sets, respectively. However, the prediction model coefficient of determination (R 2) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. The developed predictive model can predict WAG incremental recovery factor versus multiple input parameters that include rock type, WAG process type, hydrocarbon pore volume of injected gas, reservoir permeability, oil gravity, oil viscosity, reservoir pressure, and reservoir temperature. The results of the study demonstrated that few input parameters have a significant impact on WAG incremental recovery factor as reservoir permeability and hydrocarbon pore volume of injected gas. This research study uses a novel approach in pre-defining the expected incremental WAG recovery factor before committing resources for building complex numerical reservoir simulation models and running WAG pilot tests, which are very time-consuming and costly and require extensive data input.

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“…GMDH was developed to produce a model by looking only at input data and the desired output (Semenov et al 2010).…”

Section: Group Methods Of Data Handlingmentioning

confidence: 99%

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

Belazreg

Mahmood

2019

J Petrol Explor Prod Technol

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“…It produces a model looking at the input predictors and the response only. 18 The original vectors are used to build the first neural network layer, using an iterative polynomial regression procedure, with each layer feeding its output vectors to the next layer. The GMDH external criterion preserves superior neurons within each layer for successive generations, yielding an optimum neural network structure.…”

Section: ■ Machine Learningmentioning

confidence: 99%

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

2021

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Predicting the incremental recovery factor with an enhanced oil recovery (EOR) technique is a very crucial task. It requires a significant investment and expert knowledge to evaluate the EOR incremental recovery factor, design a pilot, and upscale pilot result. Water-alternating-gas (WAG) injection is one of the proven EOR technologies, with an incremental recovery factor typically ranging from 5 to 10%. The current approach of evaluating the WAG process, using reservoir modeling, is a very time-consuming and costly task. The objective of this research is to develop a fast and cost-effective mathematical model for evaluating hydrocarbon-immiscible WAG (HC-IWAG) incremental recovery factor for medium-to-light oil in undersaturated reservoirs, designing WAG pilots, and upscaling pilot results. This integrated research involved WAG literature review, WAG modeling, and selected machine learning techniques. The selected machine learning techniques are stepwise regression and group method of data handling. First, the important parameters for the prediction of the WAG incremental recovery factor were selected. This includes reservoir properties, rock and fluid properties, and WAG injection scheme. Second, an extensive WAG and waterflood modeling was carried out involving more than a thousand reservoir models. Third, WAG incremental recovery factor mathematical predictive models were developed and tested, using the group method of data handling and stepwise regression techniques. HC-IWAG incremental recovery factor mathematical models were developed with a coefficient of determination of about 0.75, using 13 predictors. The developed WAG predictive models are interpretable and user-friendly mathematical formulas. These developed models will help the subsurface teams in a variety of ways. They can be used to identify the best candidates for WAG injection, evaluate and optimize the WAG process, help design successful WAG pilots, and facilitate the upscaling of WAG pilot results to full-field scale. All this can be accomplished in a short time at a low cost and with reasonable accuracy.

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“…The statistical parameters such as correlation coefficient (R) and root mean square error (RMSE) was adopted to compare the performance of the GMDH and ANN permeability models. The mathematical expression for R and RMSE are provided in Equations (11) and (12), respectively [50] [51].…”

Section: Performance Indicatorsmentioning

confidence: 99%

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

et al. 2020

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Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

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Application of Group Method of Data Handling for Geological Modeling of Vankor Field

Cited by 3 publications

References 1 publication

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

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