Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm

2021 ◽  
Vol 196 ◽  
pp. 107995
Author(s):  
Runhai Feng
Keyword(s):  
Machine Learning ◽  
Uncertainty Analysis ◽  
Well Log

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

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

Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks

2021 ◽  
Author(s):  
Aria Abubakar ◽  
Mandar Kulkarni ◽  
Anisha Kaul
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Gamma Ray ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Well Log ◽  
Machine Learning Model ◽  
Well Correlation ◽  
Geological Formations

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.

scholarly journals Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

2021 ◽  
Author(s):  
Meysam Rajabi ◽  
Saeed Beheshtian ◽  
Shadfar Davoodi ◽  
Hamzeh Ghorbani ◽  
Nima Mohamadian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Test ◽  
Reservoir Rock ◽  
Well Log ◽  
Fracture Density ◽  
Particle Swarm Optimizer ◽  
Learning Machines ◽  
Performance Accuracy ◽  
Machine Learning Approach ◽  
Input Variables

AbstractOne of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

Automated Well-Log Processing and Lithology Classification by Identifying Optimal Features Through Unsupervised and Supervised Machine-Learning Algorithms

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2778-2800 ◽  
Author(s):  
Harpreet Singh ◽  
Yongkoo Seol ◽  
Evgeniy M. Myshakin
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Well Logs ◽  
Petroleum Engineering ◽  
Well Log ◽  
Classification Problems ◽  
Rock Types

Summary The application of specialized machine learning (ML) in petroleum engineering and geoscience is increasingly gaining attention in the development of rapid and efficient methods as a substitute to existing methods. Existing ML-based studies that use well logs contain two inherent limitations. The first limitation is that they start with one predefined combination of well logs that by default assumes that the chosen combination of well logs is poised to give the best outcome in terms of prediction, although the variation in accuracy obtained through different combinations of well logs can be substantial. The second limitation is that most studies apply unsupervised learning (UL) for classification problems, but it underperforms by a substantial margin compared with nearly all the supervised learning (SL) algorithms. In this context, this study investigates a variety of UL and SL ML algorithms applied on multiple well-log combinations (WLCs) to automate the traditional workflow of well-log processing and classification, including an optimization step to achieve the best output. The workflow begins by processing the measured well logs, which includes developing different combinations of measured well logs and their physics-motivated augmentations, followed by removal of potential outliers from the input WLCs. Reservoir lithology with four different rock types is investigated using eight UL and seven SL algorithms in two different case studies. The results from the two case studies are used to identify the optimal set of well logs and the ML algorithm that gives the best matching reservoir lithology to its ground truth. The workflow is demonstrated using two wells from two different reservoirs on Alaska North Slope to distinguish four different rock types along the well (brine-dominated sand, hydrate-dominated sand, shale, and others/mixed compositions). The results show that the automated workflow investigated in this study can discover the ground truth for the lithology with up to 80% accuracy with UL and up to 90% accuracy with SL, using six routine well logs [vp, vs, ρb, ϕneut, Rt, gamma ray (GR)], which is a significant improvement compared with the accuracy reported in the current state of the art, which is less than 70%.

A Machine-Learning-Based Approach to Assistive Well-Log Correlation

2019 ◽  
Vol 60 (4) ◽  
pp. 469-479
Author(s):  
Seth Brazell ◽  
◽  
Alex Bayeh ◽  
Michael Ashby ◽  
Darrin Burton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Log

Rock Type Classification Models Interpretability Using Shapley Values

2021 ◽  
Author(s):  
Anton Georgievich Voskresenskiy ◽  
Nikita Vladimirovich Bukhanov ◽  
Maria Alexandrovna Kuntsevich ◽  
Oksana Anatolievna Popova ◽  
Alexey Sergeevich Goncharov
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Causal Inference ◽  
Rock Type ◽  
Well Logs ◽  
Well Log ◽  
Classification Models ◽  
Rock Types ◽  
Shapley Values ◽  
Type Classification

Abstract We propose a methodology to improve rock type classification using machine learning (ML) techniques and to reveal causal inferences between reservoir quality and well log measurements. Rock type classification is an essential step in accurate reservoir modeling and forecasting. Machine learning approaches allow to automate rock type classification based on different well logs and core data. In order to choose the best model which does not progradate uncertainty further into the workflow it is important to interpret machine learning results. Feature importance and feature selection methods are usually employed for that. We propose an extension to existing approaches - model agnostic sensitivity algorithm based on Shapley values. The paper describes a full workflow to rock type prediction using well log data: from data preparation, model building, feature selection to causal inference analysis. We made ML models that classify rock types using well logs (sonic, gamma, density, photoelectric and resistivity) from 21 wells as predictors and conduct a causal inference analysis between reservoir quality and well logs responses using Shapley values (a concept from a game theory). As a result of feature selection, we obtained predictors which are statistically significant and at the same time relevant in causal relation context. Macro F1-score of the best obtained models for both cases is 0.79 and 0.85 respectively. It was found that the ML models can infer domain knowledge, which allows us to confirm the adequacy of the built ML model for rock types prediction. Our insight was to recognize the need to properly account for the underlying causal structure between the features and rock types in order to derive meaningful and relevant predictors that carry a significant amount of information contributing to the final outcome. Also, we demonstrate the robustness of revealed patterns by applying the Shapley values methodology to a number of ML models and show consistency in order of the most important predictors. Our analysis shows that machine learning classifiers gaining high accuracy tend to mimic physical principles behind different logging tools, in particular: the longer the travel time of an acoustic wave the higher probability that media is represented by reservoir rock and vice versa. On the contrary lower values of natural radioactivity and density of rock highlight the presence of a reservoir. The article presents causal inference analysis of ML classification models using Shapley values on 2 real-world reservoirs. The rock class labels from core data are used to train a supervised machine learning algorithm to predict classes from well log response. The aim of supervised learning is to label a small portion of a dataset and allow the algorithm to automate the rest. Such data-driven analysis may optimize well logging, coring, and core analysis programs. This algorithm can be extended to any other reservoir to improve rock type prediction. The novelty of the paper is that such analysis reveals the nature of decisions made by the ML model and allows to apply truly robust and reliable petrophysics-consistent ML models for rock type classification.

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