NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR

Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may span over adjacent shale beds around a target reservoir, which are frequently washed out. These washed out sections may seriously impact logging measurements of interest, such as bulk density and acoustic compressional slowness, which are used to generate elastic properties and compute geomechanical curves. Two classifications of machine learning algorithms for identifying outliers and poor-quality data due to bad hole conditions are discussed: supervised and unsupervised learning. The first allows the expert to train a model from existing and categorized data, whereas unsupervised learning algorithms learn from a collection of unlabeled data. Each classification type has distinct advantages and disadvantages. Identifying outliers and conditioning well logs prior to a petrophysical analysis or machine learning model can be a time-consuming and laborious process, especially when large multi-well datasets are considered. In this study, a new supervised learning algorithm is presented that utilizes multiple-linear regression analysis to repair well log data in an iterative and automated routine. This technique allows outliers to be identified and repaired whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically repair various well logs. A clear improvement in efficiency is observed when the algorithm is compared to other currently used methods. These include manual processing by a petrophysicist and unsupervised outlier detection methods. The algorithm can also be leveraged over multiple wells to produce more generalized predictions. Through a platform created to quickly identify and repair invalid log data, the results are controlled through input and supervision by the user. This methodology is not a direct replacement of an expert interpreter, but complementary by allowing the petrophysicist to leverage computing power, improve consistency, reduce error and improve turnaround time.

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

Energies ◽

10.3390/en14071896 ◽

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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Direct prediction of petrophysical and petroelastic reservoir properties from seismic and well-log data using nonlinear machine learning algorithms

The Leading Edge ◽

10.1190/tle38120949.1 ◽

2019 ◽

Vol 38 (12) ◽

pp. 949-958 ◽

Cited By ~ 1

Author(s):

I. I. Priezzhev ◽

P. C. H. Veeken ◽

S. V. Egorov ◽

U. Strecker

Keyword(s):

Machine Learning ◽

Bulk Density ◽

Learning Algorithms ◽

Seismic Inversion ◽

Machine Learning Algorithms ◽

Well Log ◽

Convolution Operators ◽

Reservoir Properties ◽

Log Data ◽

Linear Convolution

An analytical comparison of seismic inversion with several multivariate predictive techniques is made. Statistical data reduction techniques are examined that incorporate various machine learning algorithms, such as linear regression, alternating conditional expectation regression, random forest, and neural network. Seismic and well-log data are combined to estimate petrophysical or petroelastic properties, like bulk density. Currently, spatial distribution and estimation of reservoir properties is leveraged by inverting 3D seismic data calibrated to elastic properties (VP, VS, and bulk density) obtained from well-log data. Most commercial seismic inversions are based on linear convolution, i.e., one-dimensional models that involve a simplified plane-parallel medium. However, in cases that are geophysically more complex, such as fractured and/or fluid-rich layers, the conventional straightforward prediction relationship breaks down. This is because linear convolution operators no longer adequately describe seismic wavefield propagation due to nonlinear energy absorption. Such nonlinearity is also suggested by the seismic nonstationarity phenomenon, expressed by vertical and horizontal changes in the shape of the seismic wavelet (amplitude and frequency variations). The nonlinear predictive operator, extracted by machine learning algorithms, makes it possible in certain cases to estimate petrophysical reservoir properties more accurately and with less influence of interpretational bias.

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Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

Natural Resources Research ◽

10.1007/s11053-021-09852-2 ◽

2021 ◽

Author(s):

Mohammad Farsi ◽

Nima Mohamadian ◽

Hamzeh Ghorbani ◽

David A. Wood ◽

Shadfar Davoodi ◽

...

Keyword(s):

Machine Learning ◽

Pore Pressure ◽

Optimization Algorithms ◽

Well Log ◽

Log Data ◽

Hybrid Machine ◽

Formation Pore Pressure

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A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07252-7 ◽

2021 ◽

Author(s):

Mert Gülçür ◽

Ben Whiteside

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Multiple Linear Regression ◽

Injection Moulding ◽

Industry 4.0 ◽

Supervised Machine Learning ◽

Process Conditions ◽

Learning Models ◽

Micro Injection Moulding ◽

Machine Learning Models

AbstractThis paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

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CAMS-Net: The Clean Air Monitoring and Solutions Network

10.5194/egusphere-egu21-13912 ◽

2021 ◽

Author(s):

Daniel Westervelt ◽

Celeste McFarlane ◽

Faye McNeill ◽

R (Subu) Subramanian ◽

Mike Giordano ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Linear Regression ◽

Multiple Linear Regression ◽

Low Cost ◽

Air Monitoring ◽

Quality Data ◽

Calibration Methods ◽

The World ◽

Clean Air

There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers.&#160;Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r2 = 0.88), but have a mean bias of approximately 12 &#956;g m-3. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 &#956;g m-3 to -1.84 &#181;g m-3 or less and improve the the r2 from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries.&#160;&#160;&#160;

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A Review on Linear Regression Comprehensive in Machine Learning

Journal of Applied Science and Technology Trends ◽

10.38094/jastt1457 ◽

2020 ◽

Vol 1 (4) ◽

pp. 140-147

Author(s):

Dastan Maulud ◽

Adnan M. Abdulazeez

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Linear Relationship ◽

Multiple Regression ◽

Polynomial Regression ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Explanatory Variables ◽

Almost All ◽

Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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SeisBench: A toolbox for benchmarking and applying machine learning in seismology.

10.5194/egusphere-egu21-12218 ◽

2021 ◽

Author(s):

Jack Woollam ◽

Jannes Münchmeyer ◽

Carlo Giunchi ◽

Dario Jozinovic ◽

Tobias Diehl ◽

...

Keyword(s):

Machine Learning ◽

Model Comparison ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Quality Data ◽

Data Sets ◽

Waveform Data ◽

Detection Techniques ◽

Benchmark Data

Machine learning methods have seen widespread adoption within the seismological community in recent years due to their ability to effectively process large amounts of data, while equalling or surpassing the performance of human analysts or classic algorithms. In the wider machine learning world, for example in imaging applications, the open availability of extensive high-quality datasets for training, validation, and the benchmarking of competing algorithms is seen as a vital ingredient to the rapid progress observed throughout the last decade. Within seismology, vast catalogues of labelled data are readily available, but collecting the waveform data for millions of records and assessing the quality of training examples is a time-consuming, tedious process. The natural variability in source processes and seismic wave propagation also presents a critical problem during training. The performance of models trained on different regions, distance and magnitude ranges are not easily comparable. The inability to easily compare and contrast state-of-the-art machine learning-based detection techniques on varying seismic data sets is currently a barrier to further progress within this emerging field. We present SeisBench, an extensible open-source framework for training, benchmarking, and applying machine learning algorithms. SeisBench provides access to various benchmark data sets and models from literature, along with pre-trained model weights, through a unified API. Built to be extensible, and modular, SeisBench allows for the simple addition of new models and data sets, which can be easily interchanged with existing pre-trained models and benchmark data. Standardising the access of varying quality data, and metadata simplifies comparison workflows, enabling the development of more robust machine learning algorithms. We initially focus on phase detection, identification and picking, but the framework is designed to be extended for other purposes, for example direct estimation of event parameters. Users will be able to contribute their own benchmarks and (trained) models. In the future, it will thus be much easier to compare both the performance of new algorithms against published machine learning models/architectures and to check the performance of established algorithms against new data sets. We hope that the ease of validation and inter-model comparison enabled by SeisBench will serve as a catalyst for the development of the next generation of machine learning techniques within the seismological community. The SeisBench source code will be published with an open license and explicitly encourages community involvement.

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