Identification of natural fractures using resistive image logs, fractal dimension and support vector machines

The purpose of this research is to apply a new approach to identify natural fractures in wells in a hydrocarbon reservoir using resistive image logs, fractal dimension and support vector machines (SVMs). The stratigraphic sequence investigated by each well is composed of Cretaceous calcareous rocks from the Catatumbo Basin, Colombia. The box counting method was applied to image logs in order to generate a curve representing variations of fractal dimension in these images throughout each well. The arithmetic mean of fractal dimension showed values ranging from 1,70 to 1,72 at the mineralized fracture intervals, and from 1,72 to 1,76 at the open fracture intervals. Morphological classification between open and mineralized natural fractures is performed using corelogs integration in a pilot well. Fractal dimension of images along with gamma rays and resistivity logs were employed as the input dataset of a SVM model identifying intervals with natural open fractures automatically, shortly after logs acquisition and previous to its interpretation by specialists. Although final results were affected by borehole conditions and logs quality, the SVM model showedaccuracy between 72,3% and 82,2% in 5 wells evaluated in the studied field.El propósito de esta investigación es aplicar un nuevo enfoque para identificar fracturas naturales en pozos de un yacimiento de hidrocarburo utilizando registros de imágenes resistivas, dimensión fractal y máquinas de soporte vectorial (MSV). La secuencia estratigráfica alcanzada por cada pozo está compuesta por rocas calcáreas cretácicas de la Cuenca del Catatumbo, Colombia. El método del conteo de cajas se aplicó a registros de imágenes, generando una curva que representa variaciones de dimensión fractal en las imágenes a lo largo de cada pozo. La media aritmética de dimensión fractal mostró valores desde 1,70 a 1,72 en intervalos con fracturas mineralizadas y desde 1,72 a 1,76 en intervalos con fracturas abiertas. La clasificación morfológica entre fracturas naturales abiertas y mineralizadas es realizada utilizando integración núcleo-registro de un pozo piloto. La dimensión fractal de las imágenes junto con registros de rayos gamma y resistividad son empleados como datos de entrada a un modelo de MSV identificando intervalos con fracturas naturales abiertas automáticamente, poco después de adquirir los registros y previo a su interpretación por especialistas. Aunque los resultados finales están afectados por condiciones del hoyo y calidad de registros, el modelo de MSV mostró exactitud entre 72,3% y 82,2% en 5 pozos evaluados del campo estudiado

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

Application of mixed and virtual reality in geoscience and engineering geology

Visual learning and efficient communication in mining and geotechnical practices is crucial, yet often challenging. With the advancement of Virtual Reality (VR) and Mixed Reality (MR) a new era of geovisualization has emerged. This thesis demonstrates the capabilities of a virtual continuum approach using varying scales of geoscience applications. An application that aids analyses of small-scale geological investigation was constructed using a 3D holographic drill core model. A virtual core logger was also developed to assist logging in the field and subsequent communication by visualizing the core in a complementary holographic environment. Enriched logging practices enhance interpretation with potential economic and safety benefits to mining and geotechnical infrastructure projects. A mine-scale model of the LKAB mine in Sweden was developed to improve communication on mining induced subsidence between geologists, engineers and the public. GPS, InSAR and micro-seismicity data were hosted in a single database, which was geovisualized through Virtual and Mixed Reality. The wide array of applications presented in this thesis illustrate the potential of Mixed and Virtual Reality and improvements gained on current conventional geological and geotechnical data collection, interpretation and communication at all scales from the micro- (e.g. thin section) to the macro- scale (e.g. mine)

Automatic feature detection and interpretation in borehole data

Detailed characterisation of the structure of subsurface fractures is greatly facilitated by digital borehole logging instruments, however, the interpretation of which is typically time-consuming and labour-intensive. Despite recent advances towards autonomy and automation, the final interpretation remains heavily dependent on the skill, experience, alertness and consistency of a human operator. Existing computational tools fail to detect layers between rocks that do not exhibit distinct fracture boundaries, and often struggle characterising cross-cutting layers and partial fractures. This research proposes a novel approach to the characterisation of planar rock discontinuities from digital images of borehole logs by using visual texture segmentation and pattern recognition techniques with an iterative adaptation of the Hough transform. This approach has successfully detected non-distinct, partial, distorted and steep fractures and layers in a fully automated fashion and at a relatively low computational cost. Borehole geometry or breakouts (e.g.borehole wall elongation or compression) and imaging tool decentralisation problem affect fracture characterisation and the quality of extracted geological parameters. This research presents a novel approach to the characterisation of distorted fracture in deformed borehole geometry by using least square ellipse fitting and modified Hough transform. This approach approach has successfully detected distorted fractures in deformed borehole geometry using simulated data. To increase the fracture detection accuracy, this research uses multi-sensor data combination by combining extracted edges from different borehole data. This approach has successfully increased true positive detection rate. Performance of the developed algorithms and the results of their application have been promising in terms of speed, accuracy and consistency when compared to manual interpretation by an expert operator. It is highly anticipated that the findings of this research will increase significantly the reliance on automatic interpretation

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

Developing A Machine Learning Based Approach For Fractured Zone Detection By Using Petrophysical Logs

Oil reservoirs are divided into three categories: carbonate (fractured), sandstone and unconventional reservoirs. Identification and modeling of fractures in fractured reservoirs are so important due to geomechanical issues, fluid flood simulation and enhanced oil recovery.Image and petrophysical logs are individual tools, run inside oil wells, to achieve physical characteristics of reservoirs, e.g. geological rock types, porosity, and permeability. Fractures could be distinguished using image logs because of their higher resolution. Image logs are an expensive and newly developed tool, so they have run in limited wells, whereas petrophysical logs are usually run inside the wells. Lack of image logs makes huge difficulties in fracture detection, as well as fracture studies. In the last decade, a few studies were done to distinguish fractured zones in oil wells, by applying data mining methods over petrophysical logs. The goal of this study was also discrimination of fractured/non-fractured zones by using machine learning techniques and petrophysical logs. To do that, interpretation of image logs was utilized to label reservoir depth of studied wells as 0 (non-fractured zone) and 1 (fractured zone). We developed four classifiers (Deep Learning, Support Vector Machine, Decision Tree, and Random Forest) and applied them to petrophysics logs to discriminate fractured/non-fractured zones. Ordered Weighted Averaging was the data fusion method that we utilized to integrate outputs of classifiers in order to achieve unique and more reliable results. Overall, the frequency of non-fractured zones is about two times of fractured zones. This leads to an imbalanced condition between two classes. Therefore, the aforementioned procedure relied on the balance/imbalance data to investigate the influence of creating a balanced situation between classes. Results showed that Random Forest and Support Vector Machines are better classifiers with above 95 percent accuracy in discrimination of fractured/non-fractured zones. Meanwhile, making a balanced situation in the wells by a higher imbalance index helps to distinguish either non-fractured or fractured zones. Through imbalance data, non-fractured zones (dominant class) could be perfectly distinguished, while a significant percentage of fractured zones were also labeled as non-fractured ones

Maximizing the value of information from high-frequency downhole dynamics data

Downhole drilling dynamics are poorly understood. Neither models nor experiments seem capable of fully describing the movements and forces of the drillstring during drilling. Downhole measurements could potentially hold the key to those missing insights, however data is not yet used to its full potential. This work addresses the barriers to obtaining value from downhole dynamics data and offers solutions to overcome them. A novel kinematic model was developed that fully accounts for sensor position and measurement design. It supports the hypothesis that lateral vibrations cause high-frequency fluctuations of tangential accelerations. Hence, against currently prevailing scientific opinion, “high-frequency torsional oscillations” (HFTO) are not actually a torsional phenomenon, but the consequence of a lateral vibration. A downhole measurement tool under off-center rotation captures particular high-frequency data patterns that can be considered a sensor artifact. If ignored, these artifacts can impact the calculations of RPM and other derived measurements from downhole data. An extensive set of downhole data was analyzed to improve downhole dynamics data collection schemes for detecting drilling dysfunctions. For each prominent type of dysfunction, minimum data collection frequencies are specified. Such guidelines assist in collecting downhole data at sampling rates that are high enough to draw meaningful conclusions, but low enough to not flood limited available bandwidth and memory capacities. Even though a sensor is set up to measure only a single parameter along a single axis, it captures a variety of downhole events, which may lead to misinterpretations. These events can still be differentiated based on their typical frequency ranges. It is further shown how ‘noisy’ frequency ranges can be detected and selectively removed by combining multiple downhole measurements. A lack of transparency and inefficient processes around sensor design, data collection, processing, and transfer cause misinterpretation and under-utilization of drilling downhole data. A review of tool design and sensor identifies sources of bad data quality. Eventually, defined data quality requirements will offer sustainable sensor data improvement. To work with downhole data generated under current circumstances, data processing techniques are developed and demonstrated. Algorithms that combine data, drilling processes, and physics automatically correct sensor errors. Further, a machine learning approach for automated vibration classification based on patterns is developed. A standardized structure to transfer downhole data from the service provider to the end user is suggested. The structure does not only define how the data should be shared, but also what additional data (metadata) is required. Specifications of such informational requirements improve transparency and comparability of measurements. Therefore, the proposed data format is a prerequisite for automated drilling data analysis.Petroleum and Geosystems Engineerin

SEISMIC ATTRIBUTES ASSISTED QUANTITATIVE UNCONVENTIONAL RESERVOIRS CHARACTERIZATION

Unconventional reservoirs cover a wide range of hydrocarbon-bearing formations and reservoir types that generally do not produce economic rates of hydrocarbons without stimulation. The focus of this dissertation is to develop and calibrate workflows to aid in the characterization of the highly heterogeneous Mississippi Limestone reservoir play of northern Oklahoma. Because natural fractures and faults are the primary pathways for hydrocarbon migration and production in many reservoirs, naturally fractured reservoirs represent a significant percentage of oil and gas reservoirs throughout the world. In 2012, unconventional shale gas productions were accounting for 34% of the total natural gas production in the U.S. (0.68 trillion m3). Because of their complexity and commercial significance, naturally fractured reservoirs have been extensively studied showing that the in-situ stress field, lithology, formation thickness, structural setting and other geological factors all play a role. The understanding of the magnitude, timing, and distribution of these controlling factors could lead to an improvement in the characterization of fractures in reservoirs. Although, the geomechanical history of the rocks is elusive and often speculative, one can infer the magnitude and orientation of paleostresses and thereby hypothesize the degree of fracturing given current structure. Reservoir structure are mapped using petrophysical logs and seismic techniques where seismic attributes such as coherence and curvature are commonly used to map deformation in the subsurface. This dissertation introduces a new edge-detecting seismic attribute, aberrancy that quantitatively estimates the orientation and magnitude of poorly resolved faults as well as flexures in the subsurface. The rate of penetration (ROP) measures drilling speed, which is indicative of the overall time and in general, the cost of the drilling operation process. ROP depends on many engineering factors; however, if these parameters are held constant, ROP is a function of the geology. This dissertation is the first study that links ROP to seismic data and seismic-related attributes using proximal support vector machine. By using this workflow, we anticipate that this process can help better predict a budget or even reduce the cost of drilling when an ROP assessment is made in conjunction with reservoir quality and characteristics. Due to the complexity of fracture characterization, fracture prediction is more commonly the product of comprehensive integration of software, data, and specialize measurements specific to unconventional reservoirs. To calibrate volumetric aberrancy. I integrate seismic attributes and borehole image logs as the input to neural network. The findings on the use of volumetric aberrancy as an aid to structural interpretation and quantitative fracture prediction