Evaluation of Machine Learning Algorithms in Predicting $${\text {CO}}_2$$ Internal Corrosion in Oil and Gas Pipelines

2018 ◽  
pp. 236-254
Author(s):  
Wan Mohammad Aflah Mohammad Zubir ◽  
Izzatdin Abdul Aziz ◽  
Jafreezal Jaafar
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gas Pipelines ◽  
Internal Corrosion ◽  
Oil And Gas Pipelines

Application of machine learning algorithms to predict permeability in tight sandstone formations

Nafta-Gaz ◽  
2021 ◽  
Vol 77 (5) ◽  
pp. 283-292
Author(s):  
Tomasz Topór ◽  
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Confining Pressure ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Confining Stress ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Better Than

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.

Effect of Hydrocarbons on the Internal Corrosion of Oil and Gas Pipelines

CORROSION ◽  
2007 ◽  
Vol 63 (7) ◽  
pp. 704-712 ◽  
Author(s):  
S. Papavinasam ◽  
A. Doiron ◽  
T. Panneerselvam ◽  
R. W. Revie
Keyword(s):  
Oil And Gas ◽  
Operating Conditions ◽  
Gas Pipelines ◽  
Internal Corrosion ◽  
Oil In Water ◽  
Oil And Gas Pipelines ◽  
Water Wettability

Abstract Under certain conditions, hydrocarbons may alter the internal corrosion conditions of oil and gas pipelines. In this paper, the effects of hydrocarbons on corrosion have been predicted based on the type of emulsion (i.e., water-in-oil or oil-in-water), wettability (oil-wet, water-wet, or mixed-wet), and corrosiveness of brine in the presence of hydrocarbons. Laboratory methodologies have been developed to determine wettability and to identify the type of emulsion under pipeline operating conditions. Using these methodologies, the wettability and the type of emulsion have been determined for 14 hydrocarbons obtained from operating pipelines. The corrosiveness of brine in the presence of hydrocarbons also has been determined using rotating cage experiments.

scholarly journals Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1293
Author(s):  
Shamil Islamov ◽  
Alexey Grigoriev ◽  
Ilia Beloglazov ◽  
Sergey Savchenkov ◽  
Ove Tobias Gudmestad
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Oil And Gas ◽  
New Technologies ◽  
Learning Algorithms ◽  
Gas Production ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Drilling Process ◽  
Well Drilling

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.

scholarly journals Solving problems of the oil and gas sector using machine learning algorithms

2021 ◽  
pp. 327-337
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Oil Production ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Well Placement ◽  
Oil And Gas Sector

The article describes the tasks of the oil and gas sector that can be solved by machine learning algorithms. These tasks include the study of the interference of wells, the classification of wells according to their technological and geophysical characteristics, the assessment of the effectiveness of ongoing and planned geological and technical measures, the forecast of oil production for individual wells and the total oil production for a group of wells, the forecast of the base level of oil production, the forecast of reservoir pressures and mapping. For each task, the features of building machine learning models and examples of input data are described. All of the above tasks are related to regression or classification problems. Of particular interest is the issue of well placement optimisation. Such a task cannot be directly solved using a single neural network. It can be attributed to the problems of optimal control theory, which are usually solved using dynamic programming methods. A paper is considered where field management and well placement are based on a reinforcement learning algorithm with Markov chains and Bellman's optimality equation. The disadvantages of the proposed approach are revealed. To eliminate them, a new approach of reinforcement learning based on the Alpha Zero algorithm is proposed. This algorithm is best known in the field of gaming artificial intelligence, beating the world champions in chess and Go. It combines the properties of dynamic and stochastic programming. The article discusses in detail the principle of operation of the algorithm and identifies common features that make it possible to consider this algorithm as a possible promising solution for the problem of optimising the placement of a grid of wells.

scholarly journals A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1896
Author(s):  
Timur Merembayev ◽  
Darkhan Kurmangaliyev ◽  
Bakhbergen Bekbauov ◽  
Yerlan Amanbek
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Well Log ◽  
Hydrocarbon Production ◽  
Log Data ◽  
Reservoir Evaluation ◽  
Oil And Gas Reservoir

Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

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