Data-Driven Modeling and Prediction for Reservoir Characterization and Simulation Using Seismic and Petrophysical Data Analyses

This study explores the application of data-driven modeling and prediction in reservoir characterization and simulation using seismic and petrophysical data analyses. Different aspects of the application of data-driven modeling methods are studied, which include rock facies classification, seismic attribute analyses, petrophysical properties prediction, seismic facies segmentation, and reservoir dimension reduction. The application of using petrophysical well logs to predict rock facies is explored using different data analytics methods including decision tree, random forest, support vector machine and neural network. Different models are trained from a set of well logs and pre-interpreted rock facies data. Among the compared methods, the random forest method has the best performance in classifying rock facies in the dataset. Seismic attribute values from a 3D seismic survey and petrophysical properties from well logs are collected to explore the relationships between seismic data and well logs. In this study, deep learning neural network models are created to establish the relationships. The results show that a deep learning neural network model with multi-hidden layers is capable to predict porosity values using extracted seismic attribute values. The utilization of a set of seismic attributes improves the model performance in predicting porosity values from seismic data. This study also presents a novel deep learning approach to automatically identify salt bodies directly from seismic images. A wavelet convolutional neural network (Wavelet CNN) model, which combines wavelet transformation analyses with a traditional convolutional neural network (CNN), is developed and demonstrated to increase the accuracy in predicting salt boundaries from seismic images. The Wavelet CNN model outperforms the conventional image recognition techniques, providing higher accuracy, to identify salt bodies from seismic images. Besides, this study evaluates the effect of singular value decomposition (SVD) in dimension reduction of permeability fields during reservoir modeling. Reservoir simulation results show that SVD is valid in the parameterization of the permeability field. The reconstructed permeability fields after SVD processing are good approximations of the original permeability values. This study also evaluates the application of SVD on upscaling for reservoir modeling. Different upscaling schemes are applied on the permeability field, and their performance are evaluated using reservoir simulation

Machine Learning Assisted Framework for Advanced Subsurface Fracture Mapping and Well Interference Quantification

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., maximizing the fluid flow and capacity of the wells) is to be able to quantify and map the subsurface natural fracture systems. This needs to be done both locally, i.e., near the wellbore and generally in the scale of the wellpad, or region. In this study, the conventional near wellbore natural fracture mapping techniques is first discussed and integrated with more advanced technologies such as application of fiber optics, specifically Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS), to upscale the fracture mapping in the region. Next, a physics-based automated machine learning (AutoML) workflow is developed that incorporates the advanced data acquisition system that collects high-resolution drilling acceleration data to infer the near well bore natural fracture intensities. The new AutoML workflow aims to minimize human bias and accelerate the near wellbore natural fracture mapping in real time. The new AutoML workflow shows great promise by reducing the fracture mapping time and cost by 10-fold and producing more accurate, robust, reproducible, and measurable results. Finally, to completely remove human intervention and consequently accelerate the process of fracture mapping while drilling, the application of computer vision and deep learning techniques in new workflows to automate the process of identifying natural fractures and other lithological features using borehole image logs were integrated. Different structures and workflows have been tested and two specific workflows are designed for this purpose. In the first workflow, the fracture footprints on actual acoustic image logs (i.e., full, or partial sigmoidal signatures with a range of amplitude and vertical and horizontal displacement) is detected and classified in different categories with varying success. The second workflow implements the actual amplitude values recorded by the borehole image log and the binary representation of the produced images to detect and quantify the major fractures and beddings. The first workflow is more detailed and capable of identifying different classes of fractures albeit computationally more expensive. The second workflow is faster in detecting the major fractures and beddings. In conclusion, regional subsurface natural fracture mapping technique using an integration of conventional logging, microseismic, and fiber optic data is presented. A new AutoML workflow designed and tested in a Marcellus Shale gas reservoir was used to predict near wellbore fracture intensities using high frequency drilling acceleration data. Two integrated workflows were designed and validated using 3 wells in Marcellus Shale to extract natural fractures from acoustic image logs and amplitude recordings obtained during logging while drilling. The new workflows have: i) minimized human bias in different aspects of fracture mapping from image log analysis to machine learning model selection and hyper parameter optimization; ii) generated and quantified more accurate fracture predictions using different score matrices; iii) decreased the time and cost of the fracture interpretation by tenfold, and iv) presented more robust and reproducible results

Comparative Analysis of Artificial Intelligence and Numerical Reservoir Simulation in Marcellus Shale Wells

This dissertation addresses the limitations of conventional numerical reservoir simulation techniques in the context of unconventional shale plays and proposes the use of data-driven artificial intelligence (AI) models as a promising alternative. Traditional methods, while providing valuable insights, often rely on simplifying assumptions and are constrained by time, resources, and data quality. The research leverages AI models to handle the complexities of shale behavior more effectively, facilitating accurate predictions and optimizations with less resource expenditure. Two specific methodologies are investigated for this purpose: traditional numerical reservoir simulations using Computer Modelling Group\u27s GEM reservoir simulation software, and an AI-based Shale Analytics approach using IMPROVE™ software from Intelligent Solutions, Inc. The investigation covers the impact of key parameters on production prediction, assumptions made, predictive accuracy, data requirements, workflow complexity, and time efficiency. By comparing these methods, the research aims to offer guidelines for incorporating AI models into reservoir simulation and identify areas for increased efficiency and accuracy. The study concludes by presenting recommendations to advance the field of reservoir simulation and encourage the adoption of innovative methodologies in the energy industry. The results are anticipated to considerably enhance reservoir simulation processes and optimize production strategies for unconventional shale plays

Integrated Geomechanical Characterization of Anisotropic Gas Shales: Field Appraisal, Laboratory Testing, Viscoelastic Modelling,and Hydraulic Fracture Simulation

This research provides a multiscale geomechanical characterization workflow for ultra-tight and anisotropic Goldwyer gas shales by integrating field appraisal, laboratory deformation and ultrasonic testing, viscoelastic modelling, and hydraulic fracture simulation. The outcome of this work addresses few of the practical challenges in unconventional reservoirs including but not limited to (i) microstructure & compositional control on rock mechanical properties, (ii) robust estimation of elastic anisotropy, (iii) viscous stress relaxation to predict the least principal stress Shmin at depth from creep, (iv) influence of specific surface area on creep, and (v) impact of stress layering on hydraulic fracturing design

Machine Learning Algorithms for Predicting Reservoir Porosity using Stratigraphic-dependent Parameters

Predicting reservoir porosity, permeability and other reservoir parameters are very important but arduous task in formation evaluation, reservoir geophysics and reservoir engineering. Recent successes in machine learning and data analytics in different geoscience disciplines provides the opportunity to offer cheaper and faster techniques of predicting reservoir properties. This study used gross depositional environments, reservoir depth, diagenetic impact, permeability and stratigraphic heterogeneity from a database of 93 reservoir to predict reservoir porosity. The data for this study includes numeric and categorical descriptions of 93 reservoirs across the UK and Norwegian sector of the North Sea. Five models were trained using linear regression, support vector machine (SVM), boosted tree, bagged tree and random forest algorithms. The performance of the different models was evaluated using R-squared (R2), root mean square error (RMSE) and mean absolute error (MAE). Model trained using random forest algorithm with R2 score of 0.75, RMSE of 0.118 and MAE of 0.0028 outperformed other models. A comparison between predicted porosity and the actual porosity in training data and testing data show a good match, indicating the ability of the random forest model to make prediction on unseen data. The machine learning technique presented in this study represents a pragmatic approach to the classical log conversion problem that over the years has caused dilemmas to generations of geoscientists and petroleum engineers. The method requires no underlying mathematical models or costly assumptions of linearity among variables. Predicting porosity by using sedimentological parameters can effectively reduce the high cost of using petrophysical methods such as nuclear magnetic resonance and other logging methods

Synthetic geomechanical logs and distributions for marcellus shale

The intent of this study is to generate synthetic Geomechanical Logs for a specific Marcellus Shale asset using Artificial Intelligence and Data mining Technology. Geomechanical Distributions (Map and Volume) for the entire Marcellus Shale asset was completed. In order to accomplish the objectives, conventional well logs such as Gamma Ray and Bulk Density are used to build Data-Driven models. The Data-Driven technique used in this study is applicable to other shale reservoirs.;Successful recovery of hydrocarbons from the reservoirs, notably shale, is attributed to realizing the key fundamentals of reservoir rock properties. Having adequate and sufficient information regarding the variable lithology and mineralogy is crucial in order to identify the right pay-zone intervals for shale gas production. In addition, contribution of mechanical properties (Principal stress profiles) of shale to hydraulic fracturing strategies is a well-understood concept. It may also contribute to better, more accurate simulation models of production from shale gas reservoirs.;In this study, synthetic Geomechanical logs (Including following properties: Poisson\u27s Ratio, Total Minimum Horizontal Stress, Bulk and Shear Modulus, etc.) are developed for more than 50 Marcellus Shale wells. Using Artificial Intelligence and Data Mining (AI&DM), data-driven models are developed that are capable of generating synthetic Geomechanical logs from conventional logs such as Gamma Ray and Density Porosity. The data-driven models are validated using wells with actual Geomechanical logs that have been removed from the database to serve as blind validation wells. In addition, having access to necessary data to building Geomechanical distributions (Map and Volume) model can assist in understanding the rock mechanical behavior and consequently creating effective hydraulic fractures that is considered an essential step in economically development of Shale assets.;Moreover, running Geomechanical logs on a subset of wells, but having the luxury of generating logs of similar quality for all the existing wells in a Shale asset can prove to be a sound reservoir management tool for better reservoir characterization, modeling and efficient production of Marcellus Shale reservoir