Hear from our experts
Please join us at the virtual SPWLA 62nd Annual Symposium, 2021 to share ideas and build your knowledge. LR's CTO, Nial McCollam will be delivering the keynote for the symposium and below you'll find details of the technical sessions and workshop we'll be running at the event - be sure to book your place and add them to your schedule. Please also visit us at the Lloyd's Register virtual booth or book a meeting with our subsurface experts for a demo of the new releases of IP and IC 2021.
Presenting: Technical session
Presenting: Technical sessions and workshop
SPWLA VP Technology
Organiser SPWLA Technical Sessions
Presenting: Technical session
Data Quality Considerations for Petrophysical Machine-Learning Models
The quality of well log measurements can be impacted by various issues, including missing data, tool and sensor problems, and data processing issues. As machine learning is becoming more mainstream within the petrophysics domain, it is important that the quality of the data being used within these algorithms is assessed appropriately. This paper reviews a number of common data quality issues, and their impacts on machine learning models.
Andrew McDonald, Lloyd’s Register
Tuesday 18th May
Novel Methodology for Automation of Bad Well-Log Data Identification and Repair
A significant amount of time on a petrophysical project is spent carrying out data quality control and involves a large number of manual processes. This paper presents a new automated method for identifying and repairing poor quality data. The method increases the efficiency of the data quality control stage, without compromising on data accuracy.
Ryan Banas, PetroRes Consulting; Andrew McDonald and Tegwyn J. Perkins, Lloyd’s Register
Wednesday 19th May
Automated Selection of Inputs for Log Prediction Models Using a New Feature Selection Method
This session investigates the validity of a new Automated Feature Selection (AFS) method called Experienced Eye which can be used as part of a machine learning workflow. The results are compared to other commonly used AFS methods and domain expert selections
Ravi Arkalgud, Helio Flare Ltd
Andrew McDonald and Ross Brackenridge, Lloyd’s Register
Thursday 20th May
Machine Learning and Artificial Intelligence
This workshop will focus on the applications of Artificial Intelligence (AI) and Machine Learning (ML) to the upstream O&G industry. Consisting of two half-days, the workshop will provide an introduction to machine learning, lay out sample workflows and steps for ML applications and summarise some of the use cases in the industry.
The workshop will cover both supervised and unsupervised learning and highlight applications such as QA/QC, outlier detection, facies mapping and learning complex functional mapping.
Hands-on tutorials with Python codes to analyse a publicly available data set will also be provided.
Instructors: Lalitha Venkataramanan (Schlumberger), Chicheng Xu (Aramco), Andy McDonald (LR), Vikas Jain (Schlumberger)
Date: Wednesday, 12th May 2021 and Thursday, 13th May, 2021
Time: 8:00 – Noon. Central
Abstract: DATA QUALITY CONSIDERATIONS FOR PETROPHYSICAL MACHINE LEARNING MODELS
Decades of subsurface exploration and characterisation have led to the collation and storage of large volumes of well related data. The amount of data gathered daily continues to grow rapidly as technology and recording methods improve. With the increasing adoption of machine learning techniques in the subsurface domain, it is essential that the quality of the input data is carefully considered when working with these tools. If the input data is of poor quality, the impact on precision and accuracy of the prediction can be significant. Consequently, this can impact key decisions about the future of a well or a field.
This study focuses on well log data, which can be highly multi-dimensional, diverse and stored in a variety of file formats. Well log data exhibits key characteristics of Big Data: Volume, Variety, Velocity, Veracity and Value. Well data can include numeric values, text values, waveform data, image arrays, maps, volumes, etc. All of which can be indexed by time or depth in a regular or irregular way. A significant portion of time can be spent gathering data and quality checking it prior to carrying out petrophysical interpretations and applying machine learning models. Well log data can be affected by numerous issues causing a degradation in data quality. These include missing data - ranging from single data points to entire curves; noisy data from tool related issues; borehole washout; processing issues; incorrect environmental corrections; and mislabelled data.
Having vast quantities of data does not mean it can all be passed into a machine learning algorithm with the expectation that the resultant prediction is fit for purpose. It is essential that the most important and relevant data is passed into the model through appropriate feature selection techniques. Not only does this improve the quality of the prediction, it also reduces computational time and can provide a better understanding of how the models reach their conclusion.
This paper reviews data quality issues typically faced by petrophysicists when working with well log data and deploying machine learning models. First, an overview of machine learning and Big Data is covered in relation to petrophysical applications. Secondly, data quality issues commonly faced with well log data are discussed. Thirdly, methods are suggested on how to deal with data issues prior to modelling. Finally, multiple case studies are discussed covering the impacts of data quality on predictive capability.
Abstract: 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.
Abstract: AUTOMATED SELECTION OF INPUTS FOR LOG PREDICTION MODELS USING A NEW FEATURE SELECTION METHOD
Automation is becoming an integral part of our daily lives as technology and techniques rapidly develop. Many automation workflows are now routinely being applied within the geoscience domain. The basic structure of automation and its success of modelling fundamentally hinges on the appropriate choice of parameters and speed of processing. The entire process demands that the data being fed into any machine learning model is essentially of good quality. The technological advances in well logging technology over decades have enabled the collection of vast amounts of data across wells and fields. This poses a major issue in automating petrophysical workflows. It necessitates to ensure that, the data being fed is appropriate and fit for purpose. The selection of features (logging curves) and parameters for machine learning algorithms has therefore become a topic at the forefront of related research. Inappropriate feature selections can lead erroneous results, reduced precision and have proved to be computationally expensive.
Experienced Eye (EE) is a novel methodology, derived from Domain Transfer Analysis (DTA), which seeks to identify and elicit the optimum input curves for modelling. During the EE solution process, relationships between the input variables and target variables are developed, based on characteristics and attributes of the inputs instead of statistical averages. The relationships so developed between variables can then be ranked appropriately and selected for modelling process.
This paper focuses on three distinct petrophysical data scenarios where inputs are ranked prior to modelling: prediction of continuous permeability from discrete core measurements, porosity from multiple logging measurements and finally the prediction of key geomechanical properties. Each input curve is ranked against a target feature. For each case study, the best ranked features were carried forward to the modelling stage, and the results are validated alongside conventional interpretation methods.
Ranked features were also compared between different machine learning algorithms: DTA, Neural Networks and Multiple Linear Regression. Results are compared with the available data for various case studies. The use of the new feature selection has been proven to improve accuracy and precision of prediction results from multiple modelling algorithms.