Integrating geological attributes with a multiple linear regression of geophysical well logs to estimate the permeability of carbonate reservoirs in Campos basin - Southeastern Brazil

Almeida, Paula Maria Moura de; Carrasquilla, Abel

doi:10.1190/ice2016-6267083.1

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…Linear regression has long been a topic of interest in various industries [15][16][17][18][19]. Linear regression is a statistical learning method that models the relationship between a dependent variable, y and one or multiple independent variables, x.…”

Section: Linear Regressionmentioning

confidence: 99%

Reconstruction of Flow Rate History Using Linear Regression

Negash,

Poon,

Vasant

2023

Unconventional Methods for Geoscience, Shale Gas and Petroleum in the 21st Century

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Long-term pressure and flow rate history are important for reservoir characterization and reservoir management. However, a complete set of these data are often not available due to numerous technical difficulties. Currently, datasets with missing information are omitted and not considered for further analysis. In this study, we use machine learning algorithm via linear regression for flow rate history reconstruction. Only few studies have demonstrated the application of linear regression for well testing purposes. However, pressure and flow rate data in a producing field are comparatively longer and more complex. A combination of feature extraction and linear regression was applied for long term flow rate history reconstruction. The dataset used to evaluate the performance of the proposed method was obtained from a real producing field. This study indicated the high performance of linear regression at estimating missing flow rate history using available pressure readings in the dataset. Although linear regression has the benefits of high interpretability and fast computation time, it fails to perform well in reconstructing flow rate history when there is a significant degree of variation in the flow rate and pressure data.

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Section: Linear Regressionmentioning

confidence: 99%

Reconstruction of Flow Rate History Using Linear Regression

Negash,

Poon,

Vasant

2023

Unconventional Methods for Geoscience, Shale Gas and Petroleum in the 21st Century

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“…The results of the study of this carbonate reservoir indicate that the permeability estimated by MLR method associated with reservoir zoning based on environmental energy zones is one that comes closest of laboratory data [23]. Yan et al Estimate Fractured Carbonate Reservoir Permeability by Microresistivity Imaging Logging.…”

Section: Literature Reviewmentioning

confidence: 99%

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

et al. 2017

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Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.

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Reconstruction of Missing Gas, Oil, and Water Flow-Rate Data: A Unified Physics and Data-Based Approach

Negash

Him

2020

SPE Reservoir Evaluation &Amp; Engineering

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Summary An incomplete data set of flow rate and pressure is detrimental to reservoir management and operation. It has the potential to increase uncertainty and has the potential to unfavorably affect operational and managerial decisions. Such a data set might transpire because of failure in the flowmeters, pressure gauges, and/or unrecorded shut-in periods. This study proposes and evaluates unified physics and data-based analytics for “learning” the underlying behavior of a reservoir and reconstructing missing gas, oil, and water flow rates. The proposed workflow is evaluated using real field data obtained from a North Sea reservoir. Validation is done by using a whiteness test, a goodness-of-fit test, and a novel physics-based validation using material balance and pressure back-calculation. The outcome has shown the capability and flexibility of the selected machine-learning techniques in estimating the missing flow rate on the basis of pressure responses. The features that are extracted and expanded on the basis of physics have resulted in a high-fidelity model with less computation time.

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Integrating geological attributes with a multiple linear regression of geophysical well logs to estimate the permeability of carbonate reservoirs in Campos basin - Southeastern Brazil

Cited by 4 publications

References 1 publication

Reconstruction of Flow Rate History Using Linear Regression

Reconstruction of Flow Rate History Using Linear Regression

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Reconstruction of Missing Gas, Oil, and Water Flow-Rate Data: A Unified Physics and Data-Based Approach

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