Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence.

This work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from an industry partnership with ODS-Petrodata, a business intelligence company in Aberdeen, Scotland. This research proposes statistical models that serve as a foundation for building a novel operational tool for forecasting the performance of rig drilling operations. A prototype for a tool able to forecast the future performance of a drilling operation is created using the obtained data, the statistical model and the experts' domain knowledge. This work makes the following contributions: applying K2GA and Bayesian networks to a real-world industry problem; developing a well-performing and adaptive solution to forecast oil drilling rig performance; using the knowledge of industry experts to guide the creation of competitive models; creating models able to forecast oil drilling rig performance consistently with nearly 80% forecast accuracy, using either logistic regression or Bayesian network learning using genetic algorithms; introducing the node juxtaposition analysis graph, which allows the visualisation of the frequency of nodes links appearing in a set of orderings, thereby providing new insights when analysing node ordering landscapes; exploring the correlation factors between model score and model predictive accuracy, and showing that the model score does not correlate with the predictive accuracy of the model; exploring a method for feature selection using multiple algorithms and drastically reducing the modelling time by multiple factors; proposing new fixed structure Bayesian network learning algorithms for node ordering search-space exploration. Finally, this work proposes real-world applications for the models based on current industry needs, such as recommender systems, an oil drilling rig selection tool, a user-ready rig performance forecasting software and rig scheduling tools

Problem dependent metaheuristic performance in Bayesian network structure learning.

Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven the use of heuristic algorithms for solving this kind of problem. Amajor recent focus in BN structure learning is on search and score algorithms. In these algorithms, a scoring function is introduced and a heuristic search algorithm is used to evaluate each network with respect to the training data. The optimal network is produced according to the best score evaluated. This thesis investigates a range of search and score algorithms to understand the relationship between technique performance and structure features of the problems. The main contributions of this thesis include (a) Two novel Ant Colony Optimization based search and score algorithms for BN structure learning; (b) Node juxtaposition distribution for studying the relationship between the best node ordering and the optimal BN structure; (c) Fitness landscape analysis for investigating the di erent performances of both chain score function and the CH score function; (d) A classifier method is constructed by utilizing receiver operating characteristic curve with the results on fitness landscape analysis; and finally (e) a selective o -line hyperheuristic algorithm is built for unseen BN structure learning with search and score algorithms. In this thesis, we also construct a new algorithm for producing BN benchmark structures and apply our novel approaches to a range of benchmark problems and real world problem

Estimating the economic benefits of regional ocean observing systems

We develop a methodology to estimate the potential economic benefits from new investments in regional coastal ocean observing systems in US waters, and apply this methodology to generate preliminary estimates of such benefits. The approach focuses on potential economic benefits from coastal ocean observing information within ten geographic regions encompassing all coastal waters of the United States, and within a wide range of industrial and recreational activities including recreational fishing and boating, beach recreation, maritime transportation, search and rescue operations, spill response, marine hazards prediction, offshore energy, power generation, and commercial fishing. Our findings suggest that annual benefits to users from the deployment of ocean observing systems are likely to run in the multiple $100s of millions of dollars per year. The project results should be considered first-order estimates that are subject to considerable refinement as the parameters of regional observing systems are better defined, and as our understanding of user sectors improves.Funding was provided by the Office of Naval Research under Grant No. N00014-02-1-1037

Development of real-time early gas kick detection model

Gas kicks occur during oil and gas drilling operations due to pressure imbalances between reservoir pressure and bottomhole pressure. Uncontrolled gas kick results in blowouts which has severe consequences including death of rig personnel. For deepwater, High Temperature High Pressure, and depleted wells, early gas kick detection may mean the difference between a successful drilling operation and a catastrophic drilling operation. Modeling the physics of gas kicks is therefore an important aspect of well control in order to detect kicks and raise appropriate alarms that demand remedial action from the rig team. Also important is the quantification of the amount of kick already in the annulus and an estimation of the kick front, all in real time. Various kick models have been developed over the years to model wellbore-reservoir interactions and aid early detection of gas kicks. Some of these models and simulators are numerical and analytical; others are based on extensive collection of well data of kick events to model drilling events signatures including kicks of various sizes. In general, for non-data driven models, the accuracy of models depends on the amount of simplification done and the validity of the assumptions made. Steady state, semi-steady state and transient models exist, but if accurate detection is to occur in real-time, it is crucial that transient models are used, that the assumptions are valid, and that oversimplification is avoided in order to reflect as closely as possible, the complex physics of wellbore-reservoir interactions. The important issues to consider include the type of fluid property model used, such as compositional or black oil models; the type of frictional model used, such as Power law or Bingham plastic model; the flow regime considered; slip velocity between the phases, and the extent to which first principles are applied to problem solving, as opposed to using correlations. Our study is on real-time estimation of gas kicks during drilling using a two-phase, fully implicit, transient flow model in a vertical wellbore. The wellbore and reservoir are coupled, and a pressure gradient is introduced at the bottomhole causing gas influx into the wellbore. The gas front is then monitored in real-time as it is transported in the circulating mud to the surface pits. The model equations are the mud and gas continuity equations, the momentum conservation equation as well as sub-models, consisting of state equations and two-phase flow correlations, where needed. Much of the complex physics of gas kick is modeled, and the outcome of this research provides a tool for gas kick prediction, detection and control, and also for the estimation of the volume of kick occurring at the bottomhole in real-time.Mechanical Engineerin

Accident Precursor Probabilistic Method (APPM) for modeling and assessing risk of offshore drilling blowouts – A theoretical micro-scale application

We appreciate and acknowledge the research's financial support provided by Witt O'Brien's Brazil. Adriano Ranieri and Flavio Andrade for their professional support and reference. Oliver Peters for English proof reading and revision, Rafael Perez for proof reading the article as many times as requested, and for both anonymous peer reviewers for all their valuable comments.Peer reviewedPostprin

HPHT 101-What Petroleum Engineers and Geoscientists Should Know About High Pressure High Temperature Wells Environment

On April 20, 2010, BP’s Deepwater Horizon oil rig exploded in the Gulf of Mexico. This turned out to be one of the worst environmental disasters in recent history. This high-profile blowout at the Macondo well in the Gulf of Mexico, brought the challenges and the risks of drilling into high-pressure, high-temperature (HPHT) fields increasingly into focus. New Technology, HSE regulations, new standards, such as newly recommended procedures by the American Petroleum Institute (API), and extensive training programs for the drilling crew seem to be vital in developing HPHT resources.  High-pressure high-temperature fields exist in Gulf of Mexico, North Sea, Southeast Asia, Africa and the Middle East. Almost a quarter of HPHT operations worldwide are expected to happen in the American continent particularly in North America. Major oil companies have tried to identify key challenges in HPHT development and production, and several service companies have offered many insights regarding current or planned technologies to meet these challenges. However, there are so many factors that need to be addressed and learned in order to safely overcome the challenges of drilling into and producing from HPHT oil and gas wells.Drilling into HPHT wells is a new frontier for the oil and gas industry. The growing demand for oil and gas throughout the world is driving the exploration and production industry to look for new resources. Some of these resources are located in deeper formations. According to US Minerals Management Service (MMS), over 50% of proven oil and gas reserves in the US lie below 14,000 ft. subsea. As we drill into deeper formations we will experience higher pressures and temperatures.Drilling operations in such high pressure and high temperature environments can be very challenging. Therefore, companies are compelled to meet or exceed a vast array of technical limitations as well as environmental, health and safety standards.  This paper explains the technological challenges in developing HPHT fields, deepwater drilling, completions and production considering the reports from the Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE), formerly known as the Minerals Management Service (MMS). It reviews the HPHT related priorities of National Energy Technology Laboratory (NETL), operated by the US Department of Energy (DOE), and DeepStar Committees for Technology Development for Deepwater Research

Subsea Blowout Preventer (BOP): Design, Reliability, Testing, Deployment, and Operation and Maintenance Challenges

Subsea blowout preventer (BOP) is a safety-related instrumented system that is used in underwater oil drilling to prevent the well to blowout. As oil and gas exploration moves into deeper waters and harsher environments, the setbacks related to reliable functioning of the BOP system and its subsystems remain a major concern for researchers and practitioners. This study aims to systematically review the current state-of-the-art and present a detailed description about some of the recently developed methodologies for through-life management of the BOP system. Challenges associated with the system design, reliability analysis, testing, deployment as well as operability and maintainability are explored, and then the areas requiring further research and development will be identified. A total of 82 documents published since 1980's are critically reviewed and classified according to two proposed frameworks. The first framework categorises the literature based on the depth of water in which the BOP systems operate, with a sub-categorization based on the Macondo disaster. The second framework categorises the literature based on the techniques applied for the reliability analysis of BOP systems, including Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Petri Net (PN), Markov modelling, Bayesian Network (BN), Monte Carlo Simulation (MCS), etc. Our review analysis reveals that the reliability analysis and testing of BOP has received the most attention in the literature, whereas the design, deployment, and operation and maintenance (O&M) of BOPs received the least

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

A novel engineering framework for risk assessment of Mobile Offshore Drilling Units

Natural oil and gas has become one of mankind’s most fundamental resources. Hence, the performance of mobile offshore drilling units (MODUs) under various conditions has received considerable attention. MODUs are designed, constructed, operated, and managed for harsh geographical environments, thus they are unavoidably exposed to a wide range of uncertain threats and hazards. Ensuring the operational safety of an MODU’s system is often a complex problem. The system faces hazards from many different sources which dynamically threaten its integrity and can cause catastrophic consequences at time of failure. The main purpose of this thesis is to propose a methodology to improve the current procedures used in the risk assessment of MODUs. The aim is to prevent a critical event from occurring during drilling rather than on measures that mitigate the consequences once the undesirable event has occurred. A conceptual framework has been developed in this thesis to identify a range of hazards associated with normal operational activities and rank them in order to reduce the risks of the MODU. The proposed methodology provides a rational and systematic approach to an MODU’s risk assessment; a comprehensive model is suggested to take into consideration different influences of each hazard group (HG) of an offshore system. The Fuzzy- analytic hierarchy process (AHP) is used to determine the weights of each HG. Fault tree analysis (FTA) is used to identify basic causes and their logical relationships leading to the undesired events and to calculate the probability of occurrence of each undesirable event in an MODU system. The BBN technique is used to express the causal relationships between variables in order to predict and update the occurrence probability of each undesirable event when any new evidence becomes available. Finally, an integrated Fuzzy multiple criteria decision making (MCDM) model based on the Fuzzy-AHP and a Fuzzy techniques for order preference by similarity to an ideal solution (TOPSIS) is developed to offer decision support that can help the Decision maker to set priorities for controlling the risk and improving the MODU’s safety level. All the developed models have been tested and demonstrated with case studies. An MODU’s drilling failure due to its operational scenario has been investigated and focus has been on the mud circulation system including the blowout preventer (BOP)