identification of lithology using machine learning of gas

Identification of Lithology using machine learning of gas ...

Identification of Lithology using machine learning of gas hydrate reservoir in Krishna-Godavari offshore basin, India By A. Singh, M. Ojha and K. Sain; Publisher: European Association of Geoscientists Engineers Source: Conference Proceedings, 2nd EAGE Conference on Reservoir Geoscience, Dec 2019, Volume 2019, p.1 - 3

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A Lithology Identification Approach Based on Machine ...

May 01, 2019  Identification of underground formation lithology from well-log data is an important task in petroleum exploration and engineering. Due to the cost or imprecision of some methods applied in this activity, there is a need to automate the procedure of reservoir characterization. Machine learning techniques can be efficient alternatives to lithology identification. To acquire proper performance ...

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Carbonate Lithology Identification with Machine Learning ...

Request PDF Carbonate Lithology Identification with Machine Learning Machine learning has attracted the attention of geoscientists over the years. In particular, image analysis via machine ...

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Identification of Tight Glutenite Reservoir Lithology ...

In this paper, the method of identifying lithology by machine learning is presented, which shows good effect and improves the accuracy of lithology identification. The effect is better than other methods, can be extended.Comparing with other machine learning methods to predict lithology, support vector machine (SVM) has higher prediction accuracy.

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Evaluation of machine learning methods for lithology ...

Jun 01, 2020  For each lithological group, models and lithological code were assigned for the classification of the lithology by machine learning methods. Table 1 presents the model of the division of lithologies into four groups, denominated GP (group GP), G1 (group 1), G2 (group 2), and G3 (group 3). The core images of different lithologic groups can be ...

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(PDF) Well Logging Based Lithology Identification Model ...

Jun 29, 2020  Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification.

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A Lithology Identification Approach Based on Machine ...

May 01, 2019  Machine learning techniques can be efficient alternatives to lithology identification. To acquire proper performance, usually, some parameters of these techniques should be adjusted, and this can become a hard task depending on the complexity of the underlying problem.

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Well-Logging based lithology prediction using Machine Learning

Summary In petroleum systems, geological research studies include the classification of formation lithology by using weblogging methods. With the development of computer technology and decision-making systems based on machine learning methods, interest to automate lithology classification tasks from well logging is growing. This is especially important when manually processing large amounts of ...

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Identification of Tight Glutenite Reservoir Lithology ...

In this paper, the method of identifying lithology by machine learning is presented, which shows good effect and improves the accuracy of lithology identification. The effect is better than other methods, can be extended.Comparing with other machine learning methods to predict lithology, support vector machine (SVM) has higher prediction accuracy.

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Formation Lithology Classification: Insights into Machine ...

Sep 23, 2019  With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy.

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A Data-Driven Approach for Lithology Identification Based ...

Jul 30, 2020  method, using well logs to identify lithology is a universal indirect method, which is more accurate, e ective and economical. Until now, there have been many lithology identification methods associated with logs, including the cross plotting method, traditional statistical analysis and several machine learning methods [8–10].

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Evaluation of machine learning methods for formation ...

Identification of underground formation lithology from well log data is an important task in petroleum exploration and engineering. Recently, several computational algorithms have been used for lithology identification to improve the prediction accuracy. In this paper, we evaluate five typical machine learning methods, namely the Naïve Bayes, Support Vector Machine, Artificial Neural Network ...

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The Identification Method of Igneous Rock Lithology Based ...

Igenous rock is featured with complex and multivariant lithology, its logging response is multiplicity, therefore, it is difficult to identify igneous rock lithology with logging data. To Lay the foundation for fine logging evaluation of igneous reservoir in Songnan gas field, the identification method of igneous rock lithology is researched.

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Identification of Porosity and Permeability While Drilling ...

Feb 23, 2021  The predictions of porosity and permeability from well logging data are important in oil and gas field development. Currently, many scholars use machine learning algorithms to predict reservoir properties. However, few scholars have researched the prediction of reservoir porosity and permeability while drilling. This approach requires not only a high prediction accuracy but also short model ...

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Rate of Penetration (ROP) Prediction Using Artificial ...

influenced by several factor i.e.: weight on bit, mud weight, torque, stand pipe pressure, rotation per minutes, lithology hardness, and rotation-per minute (RPM). These parameters were then predicted using the physics-based correlation such as Bourgoyne and Young (1974), Bingham (1964), and so on in order to predict the ROP.

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‪Amrita Singh‬ - ‪Google Scholar‬

Identification of Lithology using machine learning of gas hydrate reservoir in Krishna-Godavari offshore basin, India A Singh, M Ojha, K Sain 2nd EAGE Conference on

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Facies classification using unsupervised machine learning ...

Aug 22, 2020  A list of logging tools and their properties. Using a wide array of logs is useful as they not only provide information on properties such as the lithology, porosity, and electrical conductivity of a formation, but used in conjunction with each other, can boost the accuracy of lithology estimation by performing better in situations where individual logs might fail.

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Predicting the Rock Sonic Logs While Drilling by Random ...

Jul 20, 2021  Reservoir Fluids Identification Using Vp/Vs Ratio? ... oil and gas) using the Vp/Vs ratio. ... The results of this study suggest a practical use of the machine learning models in solving problems ...

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Menal Gupta - Geoscientist, Exploration Digitalization ...

Delivered a 3D seismic based lithology classification for a mixed siliciclastic and carbonate system in a frontier exploration area, offshore Nova Scotia, eastern Canada Integrated Geophysicist ...

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Hyperspectral remote sensing in lithological mapping ...

Jul 14, 2021  The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous ...

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Formation Lithology Classification: Insights into Machine ...

Sep 23, 2019  With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy.

get price

The Identification Method of Igneous Rock Lithology Based ...

Igenous rock is featured with complex and multivariant lithology, its logging response is multiplicity, therefore, it is difficult to identify igneous rock lithology with logging data. To Lay the foundation for fine logging evaluation of igneous reservoir in Songnan gas field, the identification method of igneous rock lithology is researched.

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An interpretative approach for gas zone identification and ...

An interpretative approach for gas zone identification and lithology discrimination using derivatives of λ∗ρ and µ∗ρ attributes. Shubhabrata Samantaray* and Pankaj Gupta. Reliance Industries Ltd, Petroleum Business (EP) e-mail: [email protected]*, e-mail:[email protected] Summary

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Machine learning applied to geophysical well log data by ...

Nov 28, 2018  tannistha maiti. Nov 28, 2018 8 min read. In recent years Machine Learning (ML) has become very popular and a wide range of industries are applying it to their dataset. This article is about applying ML to well-log data and to appreciate the ways ML can help in learning the lithology in a well. An offshore drilling rig.

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(PDF) Machine Learning Application to Predict Potential ...

Machine learning in oil gas can be used to improve the capabilities of this increasingly competitive sector. One of the most noticeable effects of machine learning in an industry that focuses on oil gas is how it changes the discovery ... Lithology interpretation has been done in Well-A before, so the lithology qualitatively and ...

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Lithology and rock type determination - PetroWiki - OilGas

Jun 03, 2015  In Fig. 3, the combined use of shear slowness and cased-hole neutron porosity results in enhanced-porosity determination in a complex lithology. Crossplots of V p /V s ratio vs. compressional travel time, Δt c, facilitate identification of lithology trends with respect to porosity and lithology

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Quick-look lithology from logs - AAPG Wiki

Then any offset (or residual) between the two logs is attributable to lithology or to the presence of gas. Both tools are generally calibrated in limestone units, so the compatible scale is defined for freshwater-limestone systems, with theoretical limits as follows: All Porosity (H2O)

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Seismic Lithology Prediction: A Montney Shale Gas Case ...

This lithology prediction workflow is designed to predict petrofacies type from seismic inversion attributes, while quantifying the probability of the prediction. In the following sections, we describe how this lithology prediction was carried out for a Montney shale gas project, and explain the significance of each step in the workflow. Figure 1.

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Study on Identification of Oil/Gas and Water Zones in ...

Support Vector Machines (SVM) represents a new and very promising approach to pattern recognition based on small dataset. The approach is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of ...

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Facies classification using unsupervised machine learning ...

Aug 22, 2020  A list of logging tools and their properties. Using a wide array of logs is useful as they not only provide information on properties such as the lithology, porosity, and electrical conductivity of a formation, but used in conjunction with each other, can boost the accuracy of lithology estimation by performing better in situations where individual logs might fail.

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Real-Time Prediction of Litho-Facies From Drilling Data ...

Jul 19, 2021  The lithology of the formation is known to affect the drilling operation. Litho-facies help in the quantification of the formation properties, which optimizes the drilling parameters. The proposed work uses the artificial neural network algorithm and an optimizer to develop a working model for predicting the lithology of any formation within ...

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Density-neutron log porosity - AAPG Wiki

Jul 07, 2016  Reservoirs whose pores are gas filled may have a lower porosity than the same pores filled with oil or water because gas has a lower concentration of hydrogen atoms than either oil or water. Obtaining porosities from a neutron log. Lithology, porosity, fluid

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Frontiers Centimeter-Scale Lithology and Facies ...

Jun 24, 2021  Machine-learning algorithms have been used by geoscientists to infer geologic and physical properties from hydrocarbon exploration and development wells for more than 40 years. These techniques historically utilize digital well-log information, which, like any remotely sensed measurement, have resolution limitations. Core is the only subsurface data that is true to geologic scale and ...

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Integration of Cluster Analysis and Rock Physics for the ...

Jan 04, 2021  Based on the cross-plots of Lame’s constants with density product, and Lame’s constants ratio, additional lithology discrimination was done for the identification of a clean gas-sand zone. In the clean gas-sand zone, two different pore fluids zones were identified based on V p / V s ratio, Poisson ratio, P - and S -wave impedances.

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