Artificial Intelligence-based Modeling of Interfacial Tension for Carbon Dioxide Storage (original) (raw)
Related papers
Journal of Petroleum Exploration and Production Technology, 2015
This study introduces a general regression neural network (GRNN) model consisting of a one-pass learning algorithm with a parallel structure for estimating the minimum miscibility pressure (MMP) of crude oil as a function of crude oil composition and temperature. The GRNN model was trained with 91 samples and was successfully validated with a blind testing data set of 22 samples. The MMP for six of these data samples was experimentally measured at the Petroleum Fluid Research Centre at Kuwait University. The remaining data consisted of experimental MMP data collected from the literature. The GRNN model was used to estimate the MMP from the training data set with an average absolute error of 0.2 %. The GRNN model was used to predict the MMP for the blind test data set with an average absolute error of 3.3 %. The precision of the introduced model and models in the literature was evaluated by comparing the predicted MMP values with the measured MMP values and using training and testing data sets. The GRNN model significantly outperformed the prominent models that have been published in the literature and commonly used for estimating MMP. The use of the GRNN model was reliable over a large range of crude oil compositions, impurities, and temperature conditions. The GRNN model provides a cost-effective alternative for estimating the MMP, which is commonly, measured using experimental displacement procedures that are costly and time consuming. The results provided in this study support the use of artificial neural networks for predicting the MMP of CO 2. Keywords General regression neural network Á Enhanced oil recovery Á Minimum miscibility pressure Á Carbon dioxide Á Gas injection
Artificial-Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs
SPE Reservoir Evaluation & Engineering, 2009
Determination of relative permeability data is required for almost all calculations of fluid flow in petroleum reservoirs. Water/oil relative permeability data play important roles in characterizing the simultaneous two-phase flow in porous rocks and in predicting the performance of immiscible displacement processes in oil reservoirs. They are used, among other applications, for determining fluid distributions and residual saturations, predicting future reservoir performance, and estimating ultimate recovery. Undoubtedly, these data are considered probably the most valuable information required in reservoir-simulation studies. Estimates of relative permeability are generally obtained from laboratory experiments with reservoir-core samples. In the absence of the laboratory measurement of relative permeability data, developing empirical correlations for obtaining accurate estimates of relative permeability data showed limited success, and it proved difficult, especially for carbonate reservoir rocks.
Processes
The large scale and complexity of Carbon, Capture, Storage (CCS) projects necessitates time and cost saving strategies to strengthen investment and widespread deployment of this technology. Here, we successfully demonstrate a novel geologic site characterization workflow using an Artificial Neural Network (ANN) at the Southeast Regional Carbon Anthropogenic Test in Citronelle, Alabama. The Anthropogenic Test Site occurs within the Citronelle oilfield which contains hundreds of wells with electrical logs that lack critical porosity measurements. Three new test wells were drilled at the injection site and each well was paired with a nearby legacy well containing vintage electrical logs. The test wells were logged for measurements of density porosity and cored over the storage reservoir. An Artificial Neural Network was developed, trained, and validated using patterns recognized between the between vintage electrical logs and modern density porosity measurements at each well pair. The ...
Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO2 Performance
Energies, 2017
The injection of CO2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance. This study proposed the comprehensive evaluations of a water-alternating-CO2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO2 pro...
Design and Development of An Artificial Neural Network for Estimation of Formation Permeability
SPE Computer Applications, 1995
Permeability is one of the most important characteristics of hydrocarbon bearing formations. An accurate knowledge of permeability provides petroleum engineers with a tool for efficiently managing the production process of a field. Furthermore, It is one of the most important pieces of information in the design and management of enhanced recovery operations. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, are only available from a few wells in a field, while majority of wells are logged.
Neural Computing and Applications, 2017
Permeability is an important parameter for oil and gas reservoir characterization. Permeability can be traditionally determined by well testing and core analysis. These conventional methods are very expensive and timeconsuming. Permeability estimation in heterogeneous carbonate reservoirs is a challenge task to be handled accurately. Many researches tried to relate permeability and reservoir properties using complex mathematical equations which resulted in inaccurate estimation of the formation permeability values. Permeability prediction based on well logs using artificial intelligent techniques was presented by many authors. They used several wire-line logs such as gamma ray, neutron porosity, bulk density, resistivity, sonic, spontaneous potential, hole size, depths, and other logs. The objective of this paper is to develop an artificial neural network (ANN) model that can be used to predict the permeability of heterogeneous reservoir based on three logs only, namely resistivity, bulk density, and neutron porosity. In addition to the ANN model, in this paper and for the first time a mathematical equation from the ANN model will be extracted that can be used for permeability prediction for any data set without the need for the ANN model. Also, in this study and for the first time we introduced a new term which is the mobility index that can be used effectively in the permeability prediction. Mobility index term is derived from the mobile oil saturation that occurred due to the drilling fluid filtrate invasion. The obtained results showed that ANN model gave a comparable results with support vector machine and adaptive neuro-fuzzy inference system model. The developed mathematical equation from ANN model can be used to estimate the permeability for heterogamous carbonate reservoir based only on three parameters: bulk density, neutron porosity, and mobility index. Actual core data points (1223 points) with the three logs were used to train (857 data points, 70% of the data) and test the model for unseen data (366 data points, 30% of the data). The correlation coefficient for training and testing was 0.95, and the root-mean-square error was 0.28. The developed mathematical equation will help the engineers to save time and predict the permeability with a high accuracy using inexpensive technique. Introducing the new parameter, mobility index, in the prediction process greatly improved the permeability prediction from the log data compared to the actual measured data.
Sustainability, 2019
Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artifi...
Applied Sciences
This study aims at the development of an artificial neural network (ANN) model to optimize relief well design in Pohang Basin, South Korea. Relief well design in carbon capture and geological storage (CCS) requires complex processes and excessive iterative procedures to obtain optimal operating parameters, such as CO2 injection rate, water production rate, distance between the wells, and pressure at the wells. To generate training and testing datasets for ANN model development, optimization processes for a relief well with various injection scenarios were performed. Training and testing were conducted, where the best iteration and regression were considered based on the calculated coefficient of determination (R2) and root mean square error (RMSE) values. According to validation with a 20-year injection scenario, which was not included in the training datasets, the model showed great performance with R2 values of 0.96 or higher for all the output parameters. In addition, the RMSE va...
Eng
The accurate determination of key parameters, including the CO2-hydrocarbon solubility ratio (Rs), interfacial tension (IFT), and minimum miscibility pressure (MMP), is vital for the success of CO2-enhanced oil recovery (CO2-EOR) projects. This study presents a robust machine learning framework that leverages deep neural networks (MLP-Adam), support vector regression (SVR-RBF) and extreme gradient boosting (XGBoost) algorithms to obtained accurate predictions of these critical parameters. The models are developed and validated using a comprehensive database compiled from previously published studies. Additionally, an in-depth analysis of various factors influencing the Rs, IFT, and MMP is conducted to enhance our understanding of their impacts. Compared to existing correlations and alternative machine learning models, our proposed framework not only exhibits lower calculation errors but also provides enhanced insights into the relationships among the influencing factors. The perform...