A competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs

Adeniran, Ahmed A.; Adebayo, Abdulrauf R.; Salami, Hamza Onoruoiza; Yahaya, Mohammed Onimisi; Abdulraheem, Abdulazeez

doi:10.1016/j.acags.2019.100004

Cited by 24 publications

(13 citation statements)

References 30 publications

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“…Also, ensemble methods can be implemented in parallel computing environments, which are necessary to process missing data in big datasets. These ensemble algorithms are a group of techniques that their decisions are combined in a way to optimize the execution of a specific algorithm [112]. Developing an ensemble involves of certain steps which are creating varied models and merging their estimates(see 0.0.2 Ensemble Generation).…”

Section: Ensemble Methodsmentioning

confidence: 99%

A Survey On Missing Data in Machine Learning

Emmanuel

Maupong

Mpoeleng

et al. 2021

Preprint

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Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur as a result of various factors like missing completely at random, missing at random or missing not at random. All these may be as a result of system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for. Finally, we experiment on the K nearest neighbor and random forest imputation techniques on novel power plant induced fan data and offer some possible future research direction.

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Section: Ensemble Methodsmentioning

confidence: 99%

A Survey On Missing Data in Machine Learning

Emmanuel

Maupong

Mpoeleng

et al. 2021

Preprint

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“…As such computation is simple and easily implemented, some researchers employ KNN to realize the data-driven petrophysical characterization and, finally according to the analysis of validated results, confirm the effectiveness of KNN on the prediction of reservoir parameters [23][24][25]. Since KNN is featured by a lazy learning which means all learning samples will be scanned to search out the required neighbors for each test sample, its prediction of a test dataset with a large volume will cause a serious time-consuming phenomenon, and then "KD-tree" or "Ball-tree," which will assist KNN to form a presearching path of neighbors, is commonly used in practical case [23,24]. However, even employing such tree-based pretraining, KNN still will be low-efficient in the prediction, because to obtain a stable input-output mapping, a large-volumetric learning dataset is usually required, while training more learning samples inevitably will decelerate the speed of construction and query of "KD-tree" or "Ball-tree."…”

Section: Introductionmentioning

confidence: 91%

“…KNN primarily utilizes several learning neighbors closer to the test sample to generate an approximate regression [22]. As such computation is simple and easily implemented, some researchers employ KNN to realize the data-driven petrophysical characterization and, finally according to the analysis of validated results, confirm the effectiveness of KNN on the prediction of reservoir parameters [23][24][25]. Since KNN is featured by a lazy learning which means all learning samples will be scanned to search out the required neighbors for each test sample, its prediction of a test dataset with a large volume will cause a serious time-consuming phenomenon, and then "KD-tree" or "Ball-tree," which will assist KNN to form a presearching path of neighbors, is commonly used in practical case [23,24].…”

Section: Introductionmentioning

confidence: 99%

Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs

Zhang

Gao³

et al. 2022

Geofluids

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From a general review, most petrophysical models applied for the conventional logging interpretation imply that porosity, permeability, or water saturation mathematically have a linear or nonlinear relationship with well logs, and then arguing the prediction of these three parameters actually is accessible under a regression of logging sequences. Based on this knowledge, ensemble learning technique, partially developed for fitting problems, can be regarded as a solution. Light gradient boosting machine (LightGBM) is proved as one representative of the state-of-the-art ensemble learning, thus adopted as a potential solver to predict three target reservoir characters. To guarantee the predicting quality of LightGBM, continuous restricted Boltzmann machine (CRBM) and Bayesian optimization (Bayes) are introduced as assistants to enhance the significance of input logs and the setting of employed hyperparameters. Thereby, a new hybrid predictor, named CRBM-Bayes-LightGBM, is proposed for the prediction task. To validate the working performance of the proposed predictor, the basic data derived from the member of Chang 8, Jiyuan Oilfield, Ordos Basin, Northern China, is collected to launch the corresponding experiments. Additionally, to highlight the validating effect, three sophisticated predictors, including k-nearest neighbors (KNN), support vector regression (SVR), and random forest (RF), are introduced as competitors to implement a contrast. Since ensemble learning models universally will cause an underfitting issue when dealing with a small-volumetric dataset, transfer learning in this circumstance will be employed as an aided technique for the core predictor to achieve a satisfactory prediction. Then, three experiments are purposefully designed for four validated predictors, and given a comprehensive analysis of the gained experimented results, two critical points are concluded: (1) compared to three competitors, LightGBM-cored predictor has capability to produce more reliable predicted results, and the reliability can be further improved under a usage of more learning samples; (2) transfer learning is really functional in completing a satisfactory prediction for a small-volumetric dataset and furthermore has access to perform better when serving for the proposed predictor. Consequently, CRBM-Bayes-LightGBM combined with transfer learning is solidly demonstrated by a stronger capability and an expected robustness on the prediction of porosity, permeability, and water saturation, which then clarify that the proposed predictor can be viewed as a preferential selection when geologists, geophysicists, or petrophysicists need to finalize a characterization of sandy-mud reservoirs.

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“…Also, ensemble methods can be implemented in parallel computing environments, which are necessary to process missing data in big datasets. These ensemble algorithms are a group of techniques that their decisions are combined in a way to optimize the execution of a specific algorithm [ 109 ]. Developing an ensemble involves of certain steps which are creating varied models and merging their estimates (see Ensemble Generation).…”

Section: Missing Values Approachesmentioning

confidence: 99%

A survey on missing data in machine learning

et al. 2021

View full text Add to dashboard Cite

Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.

show abstract

A competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs

Cited by 24 publications

References 30 publications

A Survey On Missing Data in Machine Learning

A Survey On Missing Data in Machine Learning

Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs

A survey on missing data in machine learning

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