A COMPARISON BETWEEN THE PERTURBED-CHAIN STATISTICAL ASSOCIATING FLUID THEORY EQUATION OF STATE AND MACHINE LEARNING MODELING APPROACHES IN ASPHALTENE ONSET PRESSURE AND BUBBLE POINT PRESSURE PREDICTION DURING GAS INJECTION

Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas−oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (R2: 0.793, RMSE: 7.5) and the SVM models (R2: 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements

Sensitive carbonate reservoir rock characterization from magnetic hysteresis curves and correlation with petrophysical properties

demonstrated how rapid, non-destructive magnetic susceptibility measurements in clastic reservoir samples correlate with several key petrophysical parameters. The present paper shows how such measurements can also be a potentially important tool for characterising carbonate samples. Typical carbonate minerals such as calcite or dolomite are diamagnetic (negative magnetic susceptibility), whilst some other carbonates such as siderite (an iron carbonate) are paramagnetic (positive magnetic susceptibility). Carbonate rock typing can be achieved via high field susceptibility measurements which indicate the diamagnetic plus paramagnetic content. Small concentrations of ferrimagnetic impurities (such as magnetite) can significantly increase the low field magnetic susceptibility values, providing a further sensitive means of distinguishing between different carbonate samples and identifying samples that may give anomalous nuclear magnetic resonance (NMR) results. Experimental magnetic susceptibility measurements demonstrated subtle differences between samples in a suite of Middle East carbonates. Low field magnetic susceptibility values from hysteresis curves indicated small concentrations of ferrimagnetic impurities in some samples. The low field values did not correlate well with key petrophysical parameters. Significantly, however, high field measurements exhibited extremely good correlations with permeability and porosity. The high field results reflected the diamagnetic and paramagnetic minerals (comprising the main rock volume and which are the main controls on the petrophysical properties), were not affected by ferrimagnetic impurities since they saturated at lower fields. Results for some North Sea carbonates were very different to the Middle East samples. Magnetic susceptibility values were substantially higher (both low field and high field), indicating increased ferrimagnetic and paramagnetic (mainly clays) concentrations in the North Sea carbonates. This generally meant that the reservoir quality of the North Sea carbonates was poorer