Using Machine Learning to Reduce Ensembles of Geological Models for Oil and Gas Exploration

The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.

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Machine-Learning in Oil and Gas Exploration: A New Approach to Geological Risk Assessment

10.3997/2214-4609.201900988 ◽

2019 ◽

Author(s):

F. Silva ◽

S. Fernandes ◽

J. Casacão ◽

C. Libório ◽

J. Almeida ◽

...

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Oil And Gas ◽

New Approach ◽

Oil And Gas Exploration

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Experiencing ProvLake to Manage the Data Lineage of AI Workflows

10.5753/sbsi.2020.13144 ◽

2020 ◽

Author(s):

Leonardo Guerreiro Azevedo ◽

Renan Souza ◽

Raphael Melo Thiago ◽

Elton Soares ◽

Marcio Moreno

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Management ◽

Oil And Gas ◽

Core Concept ◽

Data Lineage ◽

Oil And Gas Exploration ◽

Provenance Data ◽

Management Techniques ◽

Artificial Intelligence Systems

Machine Learning (ML) is a core concept behind Artificial Intelligence systems, which work driven by data and generate ML models. These models are used for decision making, and it is crucial to trust their outputs by, e.g., understanding the process that derives them. One way to explain the derivation of ML models is by tracking the whole ML lifecycle, generating its data lineage, which may be accomplished by provenance data management techniques. In this work, we present the use of ProvLake tool for ML provenance data management in the ML lifecycle for Well Top Picking, an essential process in Oil and Gas exploration. We show how ProvLake supported the validation of ML models, the understanding of whether the ML models generalize respecting the domain characteristics, and their derivation.

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Seismo-Geological Models of Stratigraphic Sequences and Their Application to Oil and Gas Exploration in West Siberia

AAPG Bulletin ◽

10.1306/bdff8358-1718-11d7-8645000102c1865d ◽

1993 ◽

Vol 77 ◽

Author(s):

MKRTCHYAN, OLEG M., Institute of Ge

Keyword(s):

Oil And Gas ◽

West Siberia ◽

Oil And Gas Exploration ◽

Geological Models ◽

Stratigraphic Sequences

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INSIGHTS ON PP AND PS REFLECTION COEFFICIENTS AT POROELASTIC SEISMIC HORIZONS

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v33i4.757 ◽

2016 ◽

Vol 33 (4) ◽

Author(s):

José Sampaio De Oliveira ◽

Jorge Leonardo Martins

Keyword(s):

Seismic Reflection ◽

Oil And Gas ◽

Reflection Coefficients ◽

Travel Times ◽

Reflection Method ◽

Oil And Gas Exploration ◽

Geological Models ◽

Seismic Reflection Method

ABSTRACT. In oil and gas exploration, the seismic reflection method aims at obtaining information from subsurface and generating geological models based on travel times and amplitudes... RESUMO. Na área de exploração de petróleo e gás, o médtodo de reflexão s´ısmica visa a obtenção de informações de subsuperfície e a construção de modelos geológicos...

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Machine learning technique for the quantitative evaluation of tight sandstone reservoirs using high-pressure mercury-injection data

10.5194/egusphere-egu2020-17007 ◽

2020 ◽

Author(s):

JingJing Liu ◽

JianChao Liu

Keyword(s):

Machine Learning ◽

High Pressure ◽

Quantitative Evaluation ◽

Oil And Gas ◽

Tight Sandstone ◽

Reservoir Evaluation ◽

Oil And Gas Exploration ◽

Unconventional Oil ◽

Unconventional Oil And Gas ◽

Sandstone Reservoirs

<p>In recent years, China's unconventional oil and gas exploration and development has developed rapidly and has entered a strategic breakthrough period. At the same time, tight sandstone reservoirs have become a highlight of unconventional oil and gas development in the Ordos Basin in China due to its industrial and strategic value. As a digital representation of storage capacity, reservoir evaluation is a vital component of tight-oil exploration and development. Previous work on reservoir evaluation indicated that achieving satisfactory results is difficult because of reservoir heterogeneity and considerable risk of subjective or technical errors. In the data-driven era, this paper proposes a machine learning quantitative evaluation method for tight sandstone reservoirs based on K-means and random forests using high-pressure mercury-injection data. This method can not only provide new ideas for reservoir evaluation, but also be used for prediction and evaluation of other aspects in the field of oil and gas exploration and production, and then provide a more comprehensive parameter basis for &#8220;intelligent oil fields&#8221;. The results show that the reservoirs could be divided into three types, and the quantitative reservoir-evaluation criteria were established. This method has strong applicability, evident reservoir characteristics, and observable discrimination. The implications of these findings regarding ultra-low permeability and complex pore structures are practical.</p>

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Automatic Building of Structured Geological Models

Journal of Computing and Information Science in Engineering ◽

10.1115/1.1884145 ◽

2005 ◽

Vol 5 (2) ◽

pp. 138-148 ◽

Cited By ~ 12

Author(s):

Sylvain Brandel ◽

Sébastien Schneider ◽

Michel Perrin ◽

Nicolas Guiard ◽

Jean-Français Rainaud ◽

...

Keyword(s):

Present Article ◽

3D Model ◽

Oil And Gas ◽

Oil And Gas Exploration ◽

Geometric Data ◽

Geological Models

The present article proposes a method to significantly improve the construction and updating of 3D geological models used for oil and gas exploration. We present a prototype of a “geological pilot” which enables monitoring the automatic building of a 3D model topologically and geologically consistent, on which geological links between objects can easily be visualized. This model can automatically be revised in case of changes in the geometric data or in the interpretation.

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