Improving Drilling Rate of Penetration Modelling Performance Using Adaptive Neuro-Fuzzy Inference Systems

Summary For any oil and gas company, well-testing and performance-monitoring programs are expensive because of the cost of equipment and personnel. In addition, it may not be possible to obtain all of the necessary data for a reservoir for a period of time because of production demand constraints or changes in surface process conditions. To overcome these challenges, there are many studies on the implementation and value of virtual flowmetering (VFM) for real-time well performance prediction without any need for a comprehensive well-testingprogram. This paper presents the VFM model using an adaptive neuro-fuzzy inference system (ANFIS) at Hai Thach-Moc Tinh (HT-MT) gas-condensate field, offshore Vietnam. The ANFIS prediction model can tune all its membership functions (MFs) and consequent parameters to formulate the given inputs to the desired output with minimum error. In addition, ANFIS is a successful technique used to process large amounts of complex time series data and multiple nonlinear inputs-outputs (Salleh et al. 2017), thereby enhancing predictability. The authors have built ANFIS models combined with large data sets, data smoothing, and k-fold cross-validation methods based on the actual historical surface parameters such as choke valve opening, surface pressure, temperature, the inlet pressure of the gas processing system, etc. The prediction results indicate that the local regression “loess” data smoothing method reduces the processing time and gives both clustering algorithms the best results among the different data preprocessing techniques [highest value of R and lowest value of mean squared error (MSE), error mean, and error standard deviation]. The k-fold cross-validation technique demonstrates the capability to avoid the overfitting phenomenon and enhance prediction accuracy for the ANFIS subtractive clustering model. The fuzzy C-mean (FCM) model in the present study can predict the gas condensate production with the smallest root MSE (RMSE) of 0.0645 and 0.0733; the highest coefficient of determination (R2) of 0.9482 and 0.9337; and the highest variance account of 0.9482 and 0.9334 for training and testing data, respectively. Applied at the HT-MT field, the model allows the rate estimation of the gas and condensate production and facilitates the virtual flowmeter workflow using the ANFIS model.

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Estimation of Laminated Reservoir Properties Using Artificial Neural Network Integration

Ibrahim

Hamdi²,

Bataee

2023

Day 1 Wed, June 14, 2023

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Due to current economic constraints on companies' capital expenses, the lack of reservoir characterization information has become a challenging issue. As a result, reservoir engineers must make the most of the available data and estimate the reservoir characteristics that are not available. Porosity is typically used to estimate permeability through classical correlations during core analysis. However, due to the effects of complex lithology and pore geometry, it becomes unreliable to predict permeability solely from porosity and using classical relationships. The objective of this study is to enhance the Tortonian reservoir description in Gamma oil field by testing the integration of Flow Zone Indicator (FZI), Artificial Neural Network (ANN), and Convergent Interpolation (CI) techniques. The study utilized data from one exploratory well and four appraisal wells to model the non-linear relationship between the Tortonian reservoir properties, calculate the effective porosity, estimate the permeability of uncured wells, and create a permeability map for the Tortonian oil reservoir. The results showed that there are three rock types in the Tortonian reservoirs, and effective porosity and permeability logs were successfully estimated. The permeability map created showed a direct relationship with the porosity map, validating the methodology. The reliability of the porosity/permeability relationship increased to 90% after using the integrated techniques presented in this study. By integrating FZI, ANN, and CI techniques, this study has successfully modeled the non-linear relationship between the porosity and permeability of the Tortonian reservoir, enabling an economical improvement in the complex reservoir description with minimum capital budget and available data.

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Improving Drilling Rate of Penetration Modelling Performance Using Adaptive Neuro-Fuzzy Inference Systems

Cited by 5 publications

References 10 publications

Integrated surveillance system for decision-making in oil production scenarios

Integrated surveillance system for decision-making in oil production scenarios

Virtual Multiphase Flowmetering Using Adaptive Neuro-Fuzzy Inference System (ANFIS): A Case Study of Hai Thach-Moc Tinh Field, Offshore Vietnam

Estimation of Laminated Reservoir Properties Using Artificial Neural Network Integration

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