scholarly journals An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

2021 ◽  
Vol 76 ◽  
pp. 82
Author(s):  
S. Mahdia Motahhari ◽  
Mehdi Rafizadeh ◽  
S. Mahmoud Reza Pishvaie ◽  
Mohammad Ahmadi
Keyword(s):  
Machine Learning ◽  
Enhanced Oil Recovery ◽  
Shannon Entropy ◽  
Oil Recovery ◽  
Oil Field ◽  
Unsupervised Machine Learning ◽  
Main Challenge ◽  
Ideal Method ◽  
Selection Of ◽  
Economic Criteria

Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran.

Application of Lithofacies Modeling for Evaluating the Effectiveness of Enhanced Oil Recovery in the Fainsk Oil Field

2018 ◽  
Vol 785 ◽  
pp. 70-76
Author(s):  
Vadim Aleksandrov ◽  
Marsel Kadyrov ◽  
Andrey Ponomarev ◽  
Denis Drugov ◽  
Olga Veduta
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Facies Analysis ◽  
Oil Production ◽  
Oil Field ◽  
Analysis Method ◽  
Sedimentary Deposits ◽  
Technological Effectiveness ◽  
Criteria For Selection ◽  
Selection Of

A lithofacies model of the Fainsk oil field YUS11 formation was built. The results of interventions for oil production stimulation and enhanced oil recovery depending on the section penetrated by wells were considered. Criteria for selection of various types of interventions in particular geophysical conditions were given, and recommendations on the selection of technologies for bottomhole zone processing (BZP) and enhanced oil recovery (EOR) were made. The research objective is to evaluate the effectiveness of interventions in terms of enhanced oil recovery, adapted to the specific features of the field geologic structure aspects. Through the use of sedimentary deposits facies analysis method, a lithofacies model of the Fainsk oil field YUS11 formation was constructed. The application of field-geologic analysis gave an option to evaluate the technological effectiveness of interventions for oil production stimulation and enhanced oil recovery depending on the reservoir units genesis penetrated by wells.

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 Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

BJS Open ◽  
2021 ◽  
Vol 5 (1) ◽  
Author(s):  
F Torresan ◽  
F Crimì ◽  
F Ceccato ◽  
F Zavan ◽  
M Barbot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Techniques ◽  
Second Order ◽  
Learning Approach ◽  
Adrenocortical Adenoma ◽  
Unsupervised Machine Learning ◽  
First Order ◽  
Main Challenge ◽  
Machine Learning Approach ◽  
Adrenocortical Tumours

Abstract Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

Modeling to support physicochemical enhancement techniques

2021 ◽  
pp. 79-90
Author(s):  
Т. A. Pospelova
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Chemical Modeling ◽  
Comparative Structure ◽  
Water Cut ◽  
Physical And Chemical ◽  
Oil And Gas Companies ◽  
The Impact ◽  
Selection Of

The article discusses ways to increase the oil recovery factor in already developed fields, special attention is paid to the methods of enhanced oil recovery. The comparative structure of oil production in Russia in the medium term is given. The experience of oil and gas companies in the application of enhanced oil recovery in the fields is analyzed and the dynamics of the growth in the use of various enhanced oil recovery in Russia is estimated. With an increase in the number of operations in the fields, the requirements for the selection of candidates inevitably increase, therefore, the work focuses on hydrodynamic modeling of physical and chemical modeling, highlights the features and disadvantages of existing simulators. The main dependences for adequate modeling during polymer flooding are given. The calculation with different concentration of polymer solution is presented, which significantly affects the water cut and further reduction of operating costs for the preparation of the produced fluid. The possibility of creating a specialized hydrodynamic simulator for low-volume chemical enhanced oil recovery is considered, since mainly simulators are applicable for chemical waterflooding and the impact is on the formation as a whole.

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