Real-time Control and Vibrations Analysis of a Completely Automated Miniaturized Rig

Drilling Automation has become an important research effort in the Oil and Gas Industry since the fall of Oil prices in 2008. The cyclical nature of our industry and the fierce competition is pushing operators and drilling service companies to either be more efficient, or fade. A miniaturized autonomous drilling machine was built for the Society of Petroleum Engineering – (SPE) DSATS 2016 Drillbotics™ International Competition with the objective of performing optimal operations in terms of rate of penetration and energy efficiency. The miniaturized rig uses state-of-the-art sensors, control algorithms, and innovative instrumentation solutions, leading to a significant amount of data to be analyzed in real-time. High-frequency data was acquired using LabVIEW and analyzed in real-time using the MATLAB programming environment. The results of the analysis are used in a closed-loop control algorithm to optimize the rate of penetration, energy efficiency and mitigate drilling equipment failures. Using real-time instrumentation data an automated step-test procedure was implemented to optimize drilling parameters on the fly. Remote control and surveillance is possible through an in-house developed web server and smartphone app. During the initial testing phase, vibration-induced dysfunctions were mitigated and a 110% rate of penetration improvement was observed compared to initial tests. In addition, control structure was enhanced with stand-alone micro controller driven controllers that improved weight on bit (WOB) and RPM control accuracy by 305%

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

Actionable Information - Research Briefs - 3 - Analysis on U.S. States COVID-19 Dashboards

Includes both static PDF version and the dynamic web version.Covid-19 dashboards developed by state authorities in the U.S. present different variables to communicate the status and evolution of the pandemic in their territories. This research brief summarizes the different platforms used to develop the dashboards. Also, the brief includes an analysis on the dashboard contents in terms of total number of variables, type of variables, number of variables per risk component (i.e. Threats, Vulnerable Systems, Impacts, States of Risk, Mitigating Strategies). Finally, the number of variables per risk component are compared to COVID-19 metrics such as daily cases, and daily deaths (per 100K population) in order to identify how the risk communication in the dashboards impact the management of the pandemic

Actionable Information - Research Briefs - 2 - U.S. and Mexico COVID-19 Dashboards

Includes both static PDF version and the dynamic web version.States in the U.S. have produced COVID-19 dashboards to communicate the status of the pandemic to the population. This research brief presents a summary of the available dashboards for the U.S. and Mexico states. Additionally a map of the U.S. is presented with the total number of variables that are reported in the state dashboard(s)

Actionable Information - Research Briefs - 5 - Summary and Assessment of Weather Information Services

Includes both static PDF version and the dynamic web version.This research brief includes a summary of various weather information services, detailing the procedures followed to obtain and generate data, and a breakdown of the information provided. Based on this information, the services were qualitatively assessed to determine the most fitting one (s) in the production of risk analytics for supply chains impacted by natural Threat

Actionable Information - Research Briefs - 4 - Literature Review on the Impact of Natural Threats on Supply Chains

Includes both static PDF version and the dynamic web version.A literature review was conducted in order to identify the most impactful effects of natural hazards on the operation of supply chains. The review considered the body of research produced about this topic including the distribution of scientific documents produced by year, and the number of mentions of different natural threats in them

R7 - Internal Report on Risk Assessment & Management Model Development V0.0

Internal report on the development steps for a Risk Assessment and Management model using Bayesian Networks. The objectives of the model include: mapping qualitatively participating processes needed to simulate prognosis and diagnosis scenarios of social, economic and environmental impacts posed by COVID19 on the U.S. trade supply chain infrastructure. To address the public health impacts of the COVID-19 pandemic on the U.S.- Mexico health supply chain systems for health infrastructure and for the health of the workforce, considering current and emerging regional social, economic and environmental Risks. To generate risk-mitigating strategies based on resiliency and sustainability supported by evidence collection and the associate risk assessment model, to address causes and effects posed by COVID19 on the U.S. trade supply chain infrastructure, U.S.- Mexico health supply chain systems for health infrastructure, and for health of the workforce between U.S. - Mexico

Actionable Information - Research Briefs - Vaccination

Includes both static PDF version and the dynamic web version.Five research problems to identify evidence sources and provide initial validation of the risk framework model and data lake have been identified. We focused on COVID-19 Vaccination in the United States and Mexico as the predominant mitigating action. In addition to reliable sources of information, we've identified preordered vaccine supply, its main supply chain elements, and the critical facilities involved in the manufacturing and distribution of millions of doses. In this document we present an initial assessment and findings for this mitigating action

COVID-19 Vaccination Supply Chains

The ongoing COVID-19 pandemic has severely disrupted supply chains in the United States; the objective of this poster is to provide relevant information to key stakeholders in academia, government, and the general public by providing evidence-based predictive models and risk analytics on the causes and effects posed by COVID-19, on the U.S. trade supply chain infrastructure. The identification and characterization of evidence depicting the dynamics of infrastructure interactions of U.S. domestic and international trade supply chains, from procurement, manufacturing, warehousing, to transportation processes, are expected to derive inferences from public sources of information, and databases following a common risk framework developed by our research group at Texas A&M University

R13 - U.S.-Mexico Taskforce to Support the Health Supply Chain Systems for Infrastructure and Workforce Threatened by the COVID19 Pandemic

The project's milestones include the integration of a triple-helix Binational Taskforce, production of spatio-temporal near real-time analytics following a risk systems approach, and publication of a monthly U.S.-Mexico COVID-19 Risk bulletin