Deep-Learning-Based Automated Stratigraphic Correlation

Abstract

Stratigraphic correlation is essential in field evaluation as it provides the necessary tops to compartmentalize the reservoir. It further contributes to other parts of the field development planning cycle such as reservoir modeling, volumetric assessment, production allocation, etc. Traditional approach of manual pairwise correlation is labor-intensive and time-consuming. This research presents a novel automated stratigraphic correlator to create well top and zonation interpretations using supervised machine learning algorithms of Convolutional- and Recurrent-Neural-Networks (CNNs and RNNs). An automated stratigraphic correlator is created that enables stratigraphic well top and zonation interpretations learned from the well logs of a subset of wells with zonation information manually provided by human experts. The method can efficiently learn the patterns and hidden information from the well logs’ sequential data, implicitly capture the domain expertise, and streamline and automate the traditional manual repetitive work. Our method supersedes existing approaches like Multiple Sequence Alignment (MSA) by incorporating domain expertise through tops/zones picked by geologists. A Bidirectional Long Short-Term Memory (BiLSTM) is used to interpret the log data, since deposition by nature is a sequential process and RNNs can intrinsically capture such series. An Inception autoencoder CNN is also applied in this workflow for stratigraphic interpretation. Reliable post-processing is also included using the predicted zone probability logs to quantify the overall confidence score of well zonation, and to correct misinterpretation when necessary using transition frequencies in log data through a linear chain graphical probabilistic model. The methodology is tested on one of the major Middle East oilfields with around 1,500 wells to prove its efficiency and capability. The overall methodology involves data pre-processing, deep learning model training and prediction, and the post-processing of model-predicted results. In this specific workflow, the machine learning targets include both the prediction of zones (multi-class classification/segmentation problem) and the prediction of well tops (edge-detection problem). Thus, a supervised multi-task learning on a single field using CNNs and RNNs is implemented to be able to perform different tasks with the same model. The inputs to the training module include trajectory logs and other measured logs such as gamma-ray, resistivity, neutron density, etc. All inputs are normalized to zero mean and unit standard deviation. For wells with missing log values, the approach can either discard it or perform data imputation to reconstruct the data using different automated algorithms. The machine learning engine uses two different algorithms (BiLSTM and Inception autoencoder CNN), with many other deep learning models tested. The training loss function includes zone categorical cross entropy loss, tops edge detection binary cross entropy loss and L2-norm regularization term. The learning rate is dynamically adjusted during training so that it is reduced when the loss is stalled. The post-processing uses the machine learning predicted zone probability logs to select the zoning sequence that maximizes overall zonation probability and treats it as the confidence score of well zonation. This dramatically helps in constraining the outcome stratigraphic interpretation by geological succession and minimizing the correlation error. The entire workflow has been applied to one major Middle East oilfield with a large number of pre-interpreted well logs, with 60% of the wells used to train the deep learning models, 20% used for validation and the rest are for blind test. Both BiLSTM and Inception autoencoder CNN show close to human-level performance in the blind test dataset. The mean absolute error of well tops interpretation after post-processing is around 3 m throughout all analyzed wells, which provided an accuracy of nearly 90% for the blind test dataset. The classification precision and accuracy also demonstrate close-to-human-level performance in the major zones with sufficient data. It has been noticed that for cases without missing data, Inception autoencoder CNN achieves best performance, while BiLSTM benefits a lot from imputation when missing data exists. The methodology automates and streamlines the originally time-consuming stratigraphic correlation process. It performs better than existing approaches through a well-developed machine learning framework with comprehensive data pre- and post-processing. The resulting stratigraphic correlation proves to be extremely reliable even with a small number of seed wells, and it requires minimal user intervention during the process. Through deep learning techniques such as transfer learning, the proposed methodology can be readily applied to other fields even with limited training data.

Correlation of Boreholes through Well Logs: Application to the Western Sector of Madrid

“…One of the main advantages of automating the correlation process is the unification of criteria in which the analyst hardly must intervene. Many of these techniques have used expert systems for many years [ 31 , 32 , 33 , 34 ]. However, in this study, the definition of geophysical stretches within the well logs has been considered more manageable for subsequently using the most classical correlation technique that is the cross-correlation, available in most calculation programs.…”

Using AI tools to fill an incomplete well log dataset: A workflow

“…In this manner, Artificial Intelligence (AI) techniques may be helpful to fill such gaps in a reliable and quantitative way. AI algorithms have been successfully applied to well log data in the geosciences for a variety of tasks, including: lithology and facies classification (Dubois et al, 2007;Tschannen et al, 2017;Imamverdiyev and Sukhostat, 2019;Sahu et al, 2019;Kumar et al, 2022), stratigraphic correlation (Wedge et al, 2019;Tokpanov et al, 2020), coal quality estimation (Zhou and O'Brien, 2016), fracture density (Zazoun, 2013), ore presence (Caté et al, 2017), or for the estimation of interesting parameters (Goutorbe et al, 2006;Konaté et al, 2015;Ważny et al, 2021). However, these studies tend to be focused on performing a certain task with the already available data and tend not to consider the issues of handling incomplete datasets.…”

A Machine Learning Based Approach to Automate Stratigraphic Correlation through Marker Determination

“…Smith and Waterman (1980), Anderson and Gaby (1983), Howell(1983), Waterman and Raymond (1987), Fang et al (1992), Edwards et al (2018), Behdad (2019), and Le et al (2019) determined a similarity between two well-log sequences using the dynamic time warping, also called dynamic waveform matching technique. Zimmermann et al (2018), Brazell et al (2019), Bakdi et al (2020), Tokpanov et al (2020), andParimontonsakul (2021) focused on applying machine learning models in stratigraphic correlation identification. This work aims to address the correlation tasks through the geoscientists' and data analytics'lens, synchronizing with the business workflow related to the stratigraphic correlation.…”