scholarly journals Smart Proxy Modeling of SACROC CO2-EOR

Fluids ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 85 ◽  
Author(s):  
Gholami Vida ◽  
Mohaghegh D. Shahab ◽  
Maysami Mohammad
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Simulation Models ◽  
Learning Technologies ◽  
Production Optimization ◽  
Reservoir Properties ◽  
Flow Models ◽  
Proxy Models ◽  
Co2 Eor ◽  
Proxy Modeling

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

scholarly journals Machine learning based co-optimization of carbon dioxide sequestration and oil recovery in CO2-EOR project

2020 ◽  
Vol 260 ◽  
pp. 120866 ◽  
Author(s):  
Junyu You ◽  
William Ampomah ◽  
Qian Sun ◽  
Eusebius Junior Kutsienyo ◽  
Robert Scott Balch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Dioxide ◽  
Oil Recovery ◽  
Carbon Dioxide Sequestration ◽  
Co2 Eor

scholarly journals Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 126 ◽  
Author(s):  
Shohreh Amini ◽  
Shahab Mohaghegh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fluid Flow ◽  
Simulation Model ◽  
Mole Fraction ◽  
Reservoir Simulation ◽  
Simulation Models ◽  
Short Period ◽  
Proxy Models ◽  
Reservoir Simulation Model

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model.

scholarly journals ARTIFICIAL NEURAL NETWORK WITH EXPERIMENTAL DESIGN: A FAST TOOL FOR RISK ANALYSIS AND FORECASTING IN AN OIL RESERVOIR, A CASE STUDY

2020 ◽  
Vol 4 (5) ◽  
pp. 40-50
Author(s):  
Nasser Alizadeh Tabrizi
Keyword(s):  
Risk Analysis ◽  
Oil Recovery ◽  
Computing Time ◽  
Simulation Models ◽  
Production Optimization ◽  
Oil Reservoir ◽  
Total Production ◽  
Proxy Model ◽  
Artificial Neural ◽  
Screening Sensitivity

Running simulation models is CPU intensive. In computing expensive tasks such as parameter screening, sensitivity and risk analysis (uncertainty analysis) and production optimization, it can be useful to establish a simple surrogate model (proxy model) that mimics the simulation model with regard to a specific target value (for example, total production) in order to reduce the computing time and to study the available uncertainties in the reservoir and their impacts on production. Artificial Neural Networks (ANN) are one of the main tools used in machine learning. The quality of the ANN as a proxy model is dependent on how the experiments that were used to make and train it are designed. In particular, it is crucial to understand the input parameters such that their respective dependencies, correlations, and ranges are incorporated in the modelling. A combination of simulation runs should be set up that can be used to train the ANN. This task is usually referred to as the design of experiments (DOE) which gives the most informative data sets to train ANN. In this study DOE was used to train the ANN in an oil reservoir under gas injection scenario and the trained ANN, in turn, was applied to create the production profiles which were further used for risk analysis. The accuracy of the results obtained in this study indicates that ANN as a proxy model combined with DOE as a sampling method for training purpose is a fast and reliable tool that can replace the simulator. This dynamic proxy model can be used for risk analysis, production optimization and production forecasting of oil reservoirs under Enhanced Oil Recovery (EOR) methods.

The Power of Predictive Analytics in Oil Field Development: Integrating Machine Learning with Reservoir Hydrocarbon Data to Enable Enhanced Oil Recovery of Hugin Formation within the Theta Vest Structure

2021 ◽  
Author(s):  
L. T. Hardanto
Keyword(s):  
Machine Learning ◽  
Fluid Flow ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Production Optimization ◽  
Oil Field ◽  
Injection Strategy ◽  
Field Development ◽  
Facies Classification ◽  
Resolution Graph

Machine learning is an algorithm based on pattern recognition and the concept that computers can learn without being programmed to perform specific tasks. Machine learning applications that are commonly used in the oil and gas companies are petrophysical estimation and well log classification, seismic structural identification, production forecasting, and artificial intelligence tasks. The goal of this study is to integrate machine learning workflows to evaluate how reservoir hydrocarbon distribution can help prospecting, field development, and production optimization, especially 4D seismic studies. Also to observe the fluid flow and to detect bypassed oil pockets changes during the production. The workflow consists of three phases: planning, execution, and delivery. The first phase consists of collecting and preprocessing wells, seismic and interpretation data. Once the plan is considered satisfactory, it will be followed by the execution that is started with data cleaning, processing, classification, and data validation. Machine learning methods are then deployed to build an electrofacies and reservoir distribution model for the Hugin Formation using Multi-Resolution Graph-Based Clustering (MRGC). After these models reach a satisfactory level, seismic attribute analysis is performed using Principal Component Analysis (PCA) and Democratic Neural Network Association (DNNA) to create a facies probability volume. The last step in this phase is to detect geobodies of oil sand and propose an infill well or injection strategy to enable the enhancement of the oil recovery. Once the machine learning results are satisfying, tthe status of the workflow will change from execution to the delivery phase to create the final project presentation. In our study, DNNA has demonstrated excellent prediction and facies classification to image a large volume encompassing some wellbores, changes in the fluid flow during production between baseline, and monitoring seismic surveys with a good Matthews correlation coefficient of 0.849554. It allows the operator to observe the dynamic processes in and around the reservoir to help the placement of infill wells more effectively, increas development and production success, reduce risk when following proposed infill wells. The integration of machine learning can also improve the understanding of hydrocarbons in the field. It shapes E&P business strategies in a way that may increase profit revenues, such as enhanced oil recovery of an effective and efficient infill well and optimizing an injection strategy.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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