A reservoir bubble point pressure prediction model using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique with trend analysis

Abstract

The bubble point pressure (Pb) could be obtained from pressure-volume-temperature (PVT) measurements; nonetheless, these measurements have drawbacks such as time, cost, and difficulties associated with conducting experiments at high-pressure-high-temperature conditions. Therefore, numerous attempts have been made using several approaches (such as regressions and machine learning) to accurately develop models for predicting the Pb. However, some previous models did not study the trend analysis to prove the correct relationships between inputs and outputs to show the proper physical behavior. Thus, this study aims to build a robust and more accurate model to predict the Pb using the adaptive neuro-fuzzy inference system (ANFIS) and trend analysis approaches for the first time. More than 700 global datasets have been used to develop and validate the model to robustly and accurately predict the Pb. The proposed ANFIS model is compared with 21 existing models using statistical error analysis such as correlation coefficient (R), standard deviation (SD), average absolute percentage relative error (AAPRE), average percentage relative error (APRE), and root mean square error (RMSE). The ANFIS model shows the proper relationships between independent and dependent parameters that indicate the correct physical behavior. The ANFIS model outperformed all 21 models with the highest R of 0.994 and the lowest AAPRE, APRE, SD, and RMSE of 6.38%, -0.99%, 0.074 psi, and 9.73 psi, respectively, as the first rank model. The second rank model has the R, AAPRE, APRE, SD, and RMSE of 0.9724, 9%, -1.58%, 0.095 psi, and 13.04 psi, respectively. It is concluded that the proposed ANFIS model is validated to follow the correct physical behavior with higher accuracy than all studied models.

Application of GMDH model to predict pore pressure

“…However, today, with the advancement of technology and a set of artificial intelligence algorithms that are accessible, cheap, and fast, it is possible to replace the old methods that are sometimes full of bugs. Among the articles that used artificial intelligence to determine and predict key parameters, the following can be mentioned: key parameters for reservoirs (Naveshki et al, 2021;Alakbari et al, 2022;Rajabi et al, 2022a;Ayoub Mohammed et al, 2022;Hassan et al, 2022;Jafarizadeh et al, 2022); drilling (Beheshtian et al, 2022;Rajabi et al, 2022c); petrophysics Gao et al, 2022;Kamali et al, 2022).…”

Predicción de la presión de burbujeo utilizando aprendizaje automático

“…Elsharkawy [4] y Gharbi y col. [5,6] estuvieron entre los primeros que utilizaron modelos de aprendizaje automático para predecir la presión de burbujeo de los fluidos de un yacimiento. Desde entonces, se han presentado muchos estudios que buscan reemplazar a los métodos tradicionales con técnicas de inteligencia artificial/aprendizaje automático debido a su exactitud, confiabilidad, rápida velocidad de respuesta y robusta capacidad de generalización [7] - [10]. En el presente estudio se utilizó la colección de algoritmos de aprendizaje automático del programa Weka [11] para predecir la presión de burbujeo de 36 muestras de petróleo y se determinó la precisión de sus resultados con los métodos de prueba validación cruzada de 10 pliegues y validación con los datos de entrenamiento.…”