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

FIELD-SCALE GENERALITY OF THE MACHINE LEARNING MODELS

Drilling performance is directly related to fundamental aspects such as drilling variables that can affect the performance of the operation, the well stability, efficiency of drilling equipment, use of new technologies and operational parameters. Approximately 30% of the total time of construction of a well corresponds to the time rotating and sliding, in this order of ideas the optimization of the rate of penetration “ROP” has a direct impact on time and cost reduction. This reduction has as an added value: making viable economically the drilling campaigns and development of the fields. That is why one of the main objectives of the operating companies is to reduce the total time in which the true depth is reached, to reduce the costs of the operation but without affecting the main objective of the well drilling operations. To consider a good performance of the operations, many factors are involved being the rate of penetration one of the most important, without leaving behind the HSE performance, the stability of the well, integrity of the formation and final cost of the project. On the other hand, the data driven machine learning models are significantly different in conception process from physics-based models. The physic-based models try to understand the problem and propose proper models resembling he problem under certain assumptions and constraints. They seek methodology to reasonably determine the results given input. On the contrary, the machine learning models consider little about the details of the problem but train a working model mapping directly from inputs (knowns) to outputs (unknowns) through a black box of neutral networks. After that, researchers try to unveil the black box to analyze what happens there and enlighten what knowledge learned from there as to improve the model interpretability. Along the project, the relevant parameters for the machine learning predictive model were chosen considering the correlation and their dependency to ROP, the model was fed up, trained, and tested with the data set of one well and its accuracy was improved using hyperparameter tunning. After it, the algorithm was tested with five different data sets keeping constant the chosen parameters. Among them it was possible to determine that the Random Forest, Gradient Boosting and K Neighbors regressor were the ones with the highest coefficient of determination and the best performance, considering that any model in general can be improved reckoning also the importance of the learned lessons or field experience from petroleum engineering knowledge to enhance the quality of the inputs and the outputs of the model

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

Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques

Data-Driven Numerical Simulation and Optimization Using Machine Learning, and Artificial Neural Networks Methods for Drilling Dysfunction Identification and Automation

Providing the necessary energy supply to a growing world and market is essential to support human social development in an environmentally friendly. The energy industry is undergoing a digital transformation and rapidly adopting advanced technologies to improve safety and productivity and reduce carbon emissions. Energy companies are convinced that applying data-driven and physics-based technologies is the economical way forward. In drilling engineering, automating components of the drilling process has seen remarkable milestones with considerable efficiency gains. However, more elegant solutions are needed to plan, simulate, and optimize the drilling process for traditional and renewable energy generation. This work contributes to such efforts, specifically in autonomous drilling optimization, real-time drilling simulation, and data-driven methods by developing: 1) a physics-based and data-driven drilling optimization and control methodologies to aid drilling operators in performing more effective decisions and optimizing the Rate of Penetration (ROP) while reducing drilling dysfunctions. 2) developing an integrated real-time drilling simulator, 3) using data-driven methodologies to identify drilling inefficiencies and improve performance. Initially, a novel drilling control systems algorithm using machine learning methods to maximize the performance of manually controlled drilling while advising was investigated. This study employs feasible non-linear control theory and data analysis to assist in data pre-analysis and evaluation. Further emphasis was spent on developing algorithms based on formation identification and Mechanical Specific Energy (MSE), simulation, and validation. Initial drilling tests were performed in a lab-scale drilling rig with improved ROP and dysfunction identification algorithms to validate the simulated performance. Ultimately, the miniaturized drilling machine was fully automated and improved with several systems to improve performance and study the dynamic behavior while drilling by designing and implementing new control algorithms to mitigate dysfunctions and optimize the rate of penetration (ROP). Secondly, to overcome some of the current limitations faced by the industry and the need for the integration of drilling simulation models and software, in which cross-domain physics are uni-fied within a single tool through the proposition and publication of an initial common open-source framework for drilling simulation and modeling. An open-source framework and platform that spans across technical drilling disciplines surpass what any single academic or commercial orga-nization can achieve. Subsequently, a complementary filter for downhole orientation estimation was investigated and developed using numerical modeling simulation methods. In addition, the prospective drilling simulator components previously discussed were used to validate, visualize, and benchmark the performance of the dynamic models using prerecorded high-frequency down-hole data from horizontal wells. Lastly, machine-learning techniques were analyzed using open, and proprietary recorded well logs to identify, derive, and train supervised learning algorithms to quickly identify ongoing or incipient vibration and loading patterns that can damage drill bits and slow the drilling process. Followed by the analysis and implementation feasibility of using these trained models into a con-tained downhole tool for both geothermal and oil drilling operations was analyzed. As such, the primary objectives of this interdisciplinary work build from the milestones mentioned above; in-corporating data-driven, probabilistic, and numerical simulation methods for improved drilling dysfunction identification, automation, and optimization

Aircraft-based measurements for the identification and quantification of sources and sinks in the carbon cycle

Improved quantification of carbon-cycle sources and sinks is an important requirement for determining mitigation strategies and modeling future climate interactions. Analytically robust measurements require high-precision instrumentation and thoughtful experimental design to produce rigorous and reproducible results despite complex and quickly changing meteorological and environmental conditions. Here, an aircraft platform equipped with a high-precision cavity ring-down spectrometer for CO2, CH4 and H2O quantification was used to acquire data from previously un-sampled sources. The aircraft mass-balance technique was used to quantify CH4 emissions from natural gas well pads in the drilling stage, which were 2-3 orders of magnitude higher than previous estimates of emissions from this stage. In addition, the first in-situ flare emission data was collected for natural gas flares in North Dakota, Pennsylvania and Texas. Flare efficiency was high for most flares, higher than assumed efficiency. However, a few flares sampled with lower efficiencies closer to the assumed flare efficiency suggest the need for characterization of operational conditions specific to operators and basins. Finally, eddy-covariance O2 and heat fluxes were measured over three east-coast forests at sites close to and far from surface eddy-covariance towers. Tower data is often used in models to represent a larger heterogeneous region. Aircraft and tower O2 and sensible heat flux agreed well, indicating that for these sites, tower data is a good approximation of the larger region, though significant variability was observed. Aircraft latent heat fluxes were routinely much larger that tower fluxes, most likely due to the influence of advection which is measured by the aircraft eddy-covariance technique, but not by towers

Automated computer vision system for real-time drilling cuttings monitoring

In rotary drilling operations, cuttings are continuously transported to the surface by drilling fluid. Real-time monitoring of cuttings and cavings is crucial for early detection and remediation of drilling problems such as stuck pipe, lost circulation, high torque and drag, reduction in rate of penetration, and other wellbore instability issues. These incidents are large contributors to drilling-related Non-Productive Time (NPT). At the current stage, a mud logger performs monitoring manually. This work proposes to use computer vision techniques to automate this procedure. To achieve this application, specific requirements should be established to design an automated machine vision system to maintain drilling safety and speed. Cuttings ramp has been identified as an ideal location to perform the measurement, where cuttings and caving are sliding down a slope at a steady speed. To accomplish this task, an intelligent image processing system must be able to track cuttings speed, measure volume, analyze size, and generate a surface model. Through a detailed review and testing of available 3D sensing techniques, a system consisting of a 2D high-resolution camera and 3D laser profile scanner was designed. By implementing image processing techniques, the cuttings speed on the ramp was estimated which was then synchronized to the 3D depth data from a laser scanner. Finally, the volume of moving cuttings was estimated and a 3D surface profile was reconstructed using point cloud data. Experimental results in the lab environment validated that such a system can be applied to quantify cuttings volume, size distribution, and reconstruct a 3D profile of cuttings and cavings. This measured result can be stored for further analysis. Overall, this work established a foundation for the design of a sophisticated real-time monitoring system for hole cleaning and wellbore risk reduction.Petroleum and Geosystems Engineerin

Stress Analysis of Operating Gas Pipeline Installed by Horizontal Directional Drilling and Pullback Force Prediction During Installation

With the development of the natural gas industry, the demand for pipeline construction has also increased. In the context of advocating green construction, horizontal directional drilling (HDD), as one of the most widely utilized trenchless methods for pipeline installation, has received extensive attention in industry and academia in recent years. The safety of natural gas pipeline is very important in the process of construction and operation. It is necessary to conduct in-depth study on the safety of the pipeline installed by HDD method. In this dissertation, motivated by the following considerations, two aspects of HDD installation are studied. First, through the literature review, one issue that has not received much attention so far is the presence of stress problem during the operation condition. Thus, two chapters (Chapters 3 and 4) in this dissertation are related to the pipe stress analysis during the operation. Regarding this problem, two cases are considered according to the fluidity of drilling fluid. The more dangerous situation is determined by comparing the pipeline stress in the two working conditions. The stress of pipeline installed by HDD method and open-cut method is also compared, and it indicates that the stress of pipeline installed by HDD method is lower. Moreover, through the analysis of influence factors and stress sensitivity, the influence degree of different parameters on pipeline stress is obtained. Secondly, literature review indicates that the accurate prediction of pullback force in HDD construction is of great significance to construction safety and construction success. However, the accuracy of current analytical methods is not high. In the context of machine learning and big data, three new hybrid data-driven models are proposed in this dissertation (Chapter 5) for near real-time pullback force prediction, including radial basis function neural network with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN-RBFNN), support vector machine using whale optimization algorithm with CEEMDAN (CEEMDAN-WOA-SVM), and a hybrid model combines random forest (RF) and CEEMDAN. Three novel models have been verified in two projects in China. It is found that the prediction accuracy is dramatically improved compared with the original analytical models (or empirical models). In addition, through the feasibility analysis, the great potential of machine learning model in near real-time prediction is proved. At the end of this dissertation, in addition to summarizing the primary conclusions, three future research directions are also pointed out: (1) stress analysis of pipelines installed by HDD in more complex situations; (2) stress analysis of pipeline during HDD construction; (3) database establishment in HDD engineering