Application of ERTS-A imagery to fracture related mine safety hazards in the coal mining industry

The author has identified the following significant results. The most important result to date is the demonstration of the special value of repetitive ERTS-1 multiband coverage for detecting previously unknown fracture lineaments despite the presence of a deep glacial overburden. The Illinois Basin is largely covered with glacial drift and few rock outcrops are present. A contribution to the geological understanding of Illinois and Indiana has been made. Analysis of ERTS-1 imagery has provided useful information to the State of Indiana concerning the surface mined lands. The contrast between healthy vegetation and bare ground as imaged by Band 7 is sharp and substantial detail can be obtained concerning the extent of disturbed lands, associated water bodies, large haul roads, and extent of mined lands revegetation. Preliminary results of analysis suggest a reasonable correlation between image-detected fractures and mine roof fall accidents for a few areas investigated. ERTS-1 applications to surface mining operations appear probable, but further investigations are required. The likelihood of applying ERTS-1 derived fracture data to improve coal mine safety in the entire Illinois Basin is suggested from studies conducted in Indiana

Development of an ontology supporting failure analysis of surface safety valves used in Oil & Gas applications

Treball desenvolupat dins el marc del programa 'European Project Semester'.The project describes how to apply Root Cause Analysis (RCA) in the form of a Failure Mode Effect and Criticality Analysis (FMECA) on hydraulically actuated Surface Safety Valves (SSVs) of Xmas trees in oil and gas applications, in order to be able to predict the occurrence of failures and implement preventive measures such as Condition and Performance Monitoring (CPM) to improve the life-span of a valve and decrease maintenance downtime. In the oil and gas industry, valves account for 52% of failures in the system. If these failures happen unexpectedly it can cause a lot of problems. Downtime of the oil well quickly becomes an expensive problem, unscheduled maintenance takes a lot of extra time and the lead-time for replacement parts can be up to 6 months. This is why being able to predict these failures beforehand is something that can bring a lot of benefits to a company. To determine the best course of action to take in order to be able to predict failures, a FMECA report is created. This is an analysis where all possible failures of all components are catalogued and given a Risk Priority Number (RPN), which has three variables: severity, detectability and occurrence. Each of these is given a rating between 0 and 10 and then the variables are multiplied with each other, resulting in the RPN. The components with an RPN above an acceptable risk level are then further investigated to see how to be able to detect them beforehand and how to mitigate the risk that they pose. Applying FMECA to the SSV mean breaking the system down into its components and determining the function, dependency and possible failures. To this end, the SSV is broken up into three sub-systems: the valve, the actuator and the hydraulic system. The hydraulic system is the sub-system of the SSV responsible for containing, transporting and pressurizing of the hydraulic fluid and in turn, the actuator. It also contains all the safety features, such as pressure pilots, and a trip system in case a problem is detected in the oil line. The actuator is, as the name implies, the sub-system which opens and closes the valve. It is made up of a number of parts such as a cylinder, a piston and a spring. These parts are interconnected in a number of ways to allow the actuator to successfully perform its function. The valve is the actual part of the system which interacts with the oil line by opening and closing. Like the actuator, this sub-system is broken down into a number of parts which work together to perform its function. After breaking down and defining each subsystem on a functional level, a model was created using a functional block diagram. Each component also allows for the defining of dependencies and interactions between the different components and a failure diagram for each component. This model integrates the three sub-systems back into one, creating a complete picture of the entire system which can then be used to determine the effects of different failures in components to the rest of the system. With this model completed we created a comprehensive FMECA report and test the different possible CPM solutions to mitigate the largest risks

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

Expert-based development of a standard in CO2 sequestration monitoring technology

Bureau of Economic Geolog

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Aspects and directions of internal arc protectio

fi=vertaisarvioitu|en=peerReviewed

Water Management Strategies for Improved Coalbed Methane Production in the Black Warrior Basin

The modern coalbed methane industry was born in the Black Warrior Basin of Alabama and has to date produced more than 2.6 trillion cubic feet of gas and 1.6 billion barrels of water. The coalbed gas industry in this area is dependent on instream disposal of co-produced water, which ranges from nearly potable sodium-bicarbonate water to hypersaline sodium-chloride water. This study employed diverse analytical methods to characterize water chemistry in light of the regional geologic framework and to evaluate the full range of water management options for the Black Warrior coalbed methane industry. Results reveal strong interrelationships among regional geology, water chemistry, and gas chemistry. Coalbed methane is produced from multiple coal seams in Pennsylvanian-age strata of the Pottsville Coal Interval, in which water chemistry is influenced by a structurally controlled meteoric recharge area along the southeastern margin of the basin. The most important constituents of concern in the produced water include chlorides, ammonia compounds, and organic substances. Regional mapping and statistical analysis indicate that the concentrations of most ionic compounds, metallic substances, and nonmetallic substances correlate with total dissolved solids and chlorides. Gas is effectively produced at pipeline quality, and the only significant impurity is N{sub 2}. Geochemical analysis indicates that the gas is of mixed thermogenic-biogenic origin. Stable isotopic analysis of produced gas and calcite vein fills indicates that widespread late-stage microbial methanogenesis occurred primarily along a CO{sub 2} reduction metabolic pathway. Organic compounds in the produced water appear to have helped sustain microbial communities. Ammonia and ammonium levels increase with total dissolved solids content and appear to have played a role in late-stage microbial methanogenesis and the generation of N{sub 2}. Gas production tends to decline exponentially, whereas water production tends to decline hyperbolically. Hyperbolic decline indicates that water volume is of greatest concern early in the life of a coalbed methane project. Regional mapping indicates that gas production is controlled primarily by the ability to depressurize permeable coal seams that are natively within the steep part of the adsorption isotherm. Water production is greatest within the freshwater intrusion and below thick Cretaceous cover strata and is least in areas of underpressure. Water management strategies include instream disposal, which can be applied effectively in most parts of the basin. Deep disposal may be applicable locally, particularly where high salinity limits the ability to dispose into streams. Artificial wetlands show promise for the management of saline water, especially where the reservoir yield is limited. Beneficial use options include municipal water supply, agricultural use, and industrial use. The water may be of use to an inland shrimp farming industry, which is active around the southwestern coalbed methane fields. The best opportunities for beneficial use are reuse of water by the coalbed methane industry for drilling and hydraulic fracturing. This research has further highlighted opportunities for additional research on treatment efficiency, the origin of nitrogen compounds, organic geochemistry, biogenic gas generation, flow modeling, and computer simulation. Results of this study are being disseminated through a vigorous technology transfer program that includes web resources, numerous presentations to stakeholders, and a variety of technical publications

Casing structural integrity and failure modes in a range of well types: a review.

This paper focus on factors attributing to casing failure, their failure mechanism and the resulting failure mode. The casing is a critical component in a well and the main mechanical structural barrier element that provide conduits and avenue for oil and gas production over the well lifecycle and beyond. The casings are normally subjected to material degradation, varying local loads, induced stresses during stimulation, natural fractures, slip and shear during their installation and operation leading to different kinds of casing failure modes. The review paper also covers recent developments in casing integrity assessment techniques and their respective limitations. The taxonomy of the major causes and cases of casing failure in different well types is covered. In addition, an overview of casing trend utilisation and failure mix by grades is provided. The trend of casing utilisation in different wells examined show deep-water and shale gas horizontal wells employing higher tensile grades (P110 & Q125) due to their characteristics. Additionally, this review presents casing failure mixed by grades, with P110 recording the highest failure cases owing to its stiffness, high application in injection wells, shale gas, deep-water and high temperature and high temperature (HPHT) wells with high failure probability. A summary of existing tools used for the assessment of well integrity issues and their respective limitations is provided and conclusions drawn

Supervisory Controller Validation For A Plug-In Parallel-Through-The-Road Hybrid Electric Vehicle By Software-In-The-Loop Testing

The goal of this research is to develop an operational supervisory controller for Wayne State University Hybrid Warriors\u27 hybrid electric vehicle architecture that can be transitioned easily to a hardware-in-the-loop testing environment for the 2011-2014 EcoCAR2 competition. It serves to demonstrate how model-based design, specifically software-in-the-loop testing, is effective for the initial steps in design, verification, and validation of a supervisory control strategy. Overall, the supervisory controller aims to meet all safety and functional requirements while reducing fuel consumption. The thesis starts by presenting a plug-in parallel-through-the-road architecture and its powertrain hardware components. Next, characteristics and capabilities of all significant powertrain components are explained along with the implementation of the vehicle plant model. Initial stages and preparations for the development of supervisory controller begin with applying the Design Failure Mode and Effects Analysis and identifying the functional vehicle requirements. Control strategies implemented within the supervisory controller are discussed in detail. Finally, results from the software-in-the-loop testing as well as safety critical fault mitigation are shown, to demonstrate the end product of a supervisory controller that has reached a high level of functionality and safety and therefore is ready for hardware-in-the-loop testing. Outlines are provided for extending the current work into next phases of hardware-in-the-loop testing, optimization using vehicle-in-the-loop results, and special applications such as cold-start