Prediction of Crude Oil Viscosity and Gas/Oil Ratio Curves Using Recent Advances to Neural Networks

Abstract

This paper proposes and implements a new approach for predicting Pressure -Volume-Temperature (PVT) properties of crude oils. Instead of the usual single or multi-data point prediction for any crude oil PVT property that is described by a curve, the approach in this study predicts such a property over a specified range of required reservoir pressures. Moreover, the shapes of the predicted curves are smooth and consistent with the experimental curves. Prediction models based on Artificial Neural Networks (ANN) and two of its advances; Support Vector Regression (SVR) and Functional Networks (FN), have been developed to execute the formulated approach. The approach has been demonstrated for viscosity and solution gas/oil ratio (GOR) curves. These two properties vary with reservoir pressures and they are often required to be estimated over a specified range of pressures. In this study, three different data sets have been used. The first Data set consists of 12 variables which are the predictors, including crude oil hydrocarbon and non-hydrocarbon compositions and some reservoir properties. The other two data sets consist of laboratory viscosity-pressure measurements and laboratory gas/oil ratio-pressure measurements for plotting the viscosity and solution GOR curves respectively. In the simulation results, SVR and FN give better performances than the conventional ANN technique. Graphical plots and two common statistical measures (root mean square error, RMSE, and absolute percent relative error, AAPRE) have been used to evaluate the performances of all the developed models. Introduction In petroleum engineering, characterization of reservoir fluids plays an important role of developing strategies for operating and managing existing reservoirs and development of new ones. These reservoir fluid properties are important in petroleum engineering computations for: simulating reservoirs, evaluating reserves, forecasting production, designing production facilities and transportation systems. Crude oil viscosity and GOR are two of these important PVT properties which are required to be calculated or estimated at different stages of crude oil exploration. These properties are traditionally determined from laboratory analyses on samples collected from the bottom of the wellbore or after recombining the liquid and vapour samples collected from the separator at the surface. To solve the problem of going through the rigorous laboratory experimentations which consume valuable production resources: time and money, equations of states (EOS) and empirically derived correlations have been used to predict these reservoir fluid properties. The two methods were used for a long period of time until Soft Computing (SC) techniques were introduced to improve the prediction performances. The mostly used SC technique in solving Petroleum Engineering is ANN. Although SC techniques have not being widely-utilised in the Petroleum Industries compared with some other fields, they have been applied successfully in some Petroleum Engineering problems with exceptional and acceptable performances. A review of applications of some of these intelligent systems and their potentials in oil and gas industry can be found in (Mohaghegh 2005). In this study, we introduce a new approach for predicting any PVT property that can be represented as a curve. All predictions have been done using ANN as well as two of its advanced algorithms: SVR and FN.

Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study

“…It was used by Bageri, Anifowose, and Abdulraheem (2015), Helmy, Fatai, and Faisal (2010), Imam, Anifowose, and Azad (2015), Khoukhi and Albukhitan (2010), nooruddin, Anifowose, and Abdulraheem (2013), olatunji, Selamat, and raheem (2011), and oloso, Khoukhi, Abdulraheem, and Elshafei (2010). Bageri et al (2015) used the predictive capabilities of Ann and the Fuzzy Inference Engine to predict the water saturation of a Middle Eastern carbonate petroleum reservoir.…”

Application of Artificial Intelligence and Data Mining Techniques for Fast Track Modeling of Geologic Sequestration of Carbon Dioxide – Case Study of SACROC Unit

“…29 Since this time, the applications of ANNs in addressing the conventional problems of the petroleum industry have been widely studied. Some applications of ANNs in petroleum engineering literature include well log interpretation, [30][31][32] well test data analysis, 33-36 reservoir characterization, 37-39 calibration of seismic attributes, 40 seismic pattern recognition, 41 inversion of seismic waveforms, 42 prediction of PVT data, [43][44][45][46] fractures and faults identification, [47][48][49][50] hydrocarbons detection, 50,51 formation damage forecast, 52,53 etc.…”

Artificial Intelligence (AI) Assisted History Matching

“…Sales forecasting (Yip, Hines and Yu 1997) Industrial process control (Devadhas, Pushpakumar and Mary 2012) Customer research (Chattopadhyay, et al 2012) Risk management (Sarcià, Cantone and Basili 2007) Credit evaluation (Baesens, et al 2003) Energy cost prediction (Yalcintas and Akkurt 2005) Medical diagnosis (Amato, et al 2013) (Lei and Xing-cheng 2010) Business applications (Li 1994) Financial applications (Tan 2004) Stock market prediction (Adebiyi, et al 2012) Reservoir characterization ) (Ahmed, et al 1997) (Singh, et al 2008) Seismic attributes calibration Seismic pattern recognition Inversion of seismic waveforms (David 1993) (Yang and Huang 1991) (Roth and Tarantoia 1992) Prediction of PVT data (Briones, et al 1994)(Gharbi and Elsharkawy 1997)(Osman, Abdel-Wahhab and Al-Marhoun 2001) (Oloso, et al 2009) Identifying fractures and faults (Key, et al 1997)(Sadiq and Nashawi 2000) (Aminzadeh and deGroot 2005) Detecting hydrocarbons and forecast formation damage (Cheng-Dang, et al 1994)(Aminzadeh and deGroot 2005) (Nikravesh, et al 1996) (Kalam, Al-Alawi and Al-Mukheini 1996)…”