Developing a risk assessment model using fuzzy logic to assess groundwater contamination from hydraulic fracturing

Technological advances in directional drilling has led to rapid exploitation of onshore unconventional hydrocarbons using a technique known as hydraulic fracturing. This process took off initially in the US, with Canada following closely behind, but brought with it controversial debates over environmental protection, particularly in relation to groundwater contamination and well integrity failure. Prospective shale gas regions lie across areas in Europe but countries such as the UK are facing public and government turmoil surrounding their potential exploitation. This extent of energy development requires detailed risk analysis to eliminate or mitigate damage to the natural environment. Subsurface energy activities involve complex processes and uncertain data, making comprehensive, quantitative risk assessments a challenge to develop. A new, alternative methodology was applied to onshore hydraulic fracturing to assess the risk of groundwater contamination during well injection and production. The techniques used deterministic models to construct failure scenarios with respect to groundwater contamination, stochastic approaches to determine component failures of a well, and fuzzy logic to address insufficiency or complexity in data. The framework was successfully developed using available data and regulations in British Columbia (BC), Canada. Fuzzy Fault Tree Analysis (FFTA) was demonstrated as a more robust technique compared with conventional Fault Tree Analysis (FTA) and implemented successfully to quantify cement failure. A collection of known risk analysis methods such as Event Tree Analysis (ETA), Time at Risk Failure (TRF) and Mean Time To Failure (MTTF) models were successfully applied to well integrity failure during injection, with the novel addition of quantifying cement failures. An analytical model for Surface Casing Pressure (SCP) during well production highlighted data gaps on well constructions so a fuzzy logic model was built to a 93% accuracy to determine the location of cement in a well. This novel application of fuzzy logic allowed the calculation of gas flow rate into an annulus and hence the probability of well integrity failure during production using ETA. The framework quantified several risk pathways across multiple stages of a well using site-specific data, but was successfully applied to a UK case study where there existed significant differences in geology, well construction and regulations. The application required little extra work and demonstrated the success and limitations of the model and where future work could improve model development. This research indicated that risks to groundwater from hydraulic fracturing differ substantially depending on well construction. Weighing up the risk to groundwater compared with financial gain for well construction will be essential for decision-makers and policy. To reduce the social anxiety of hydraulic fracturing in the UK, decision-makers who face criticism must ensure information is disseminated properly to the public with a well-defined risk analysis which can be interpreted easily without prerequisite knowledge. Finally, although this research is based on onshore hydraulic fracturing, the risk assessment techniques are generic enough to allow application of this research to other subsurface activities such as CO2 sequestration, waste injection disposal and geothermal energy.Engineering and Physical Sciences Research Council (EPSRC

Advances in Unconventional Oil and Gas

This book focuses on the latest progress in unconventional oil and gas (such as coalbed methane, shale gas, tight gas, heavy oil, hydrate, etc.) exploration and development, including reservoir characterization, gas origin and storage, accumulation geology, hydrocarbon generation evolution, fracturing technology, enhanced oil recovery, etc. Some new methods are proposed to improve the gas extraction in coal seams, characterize the relative permeability of reservoirs, improve the heat control effect of hydrate-bearing sediment, improve the development efficiency of heavy oil, increase fracturing effectiveness in tight reservoirs, etc

Dynamic safety analysis of managed pressure drilling operations

The exploration and development of oil and gas reserves located in harsh offshore environments are characterized with high risk. Some of these reserves would be uneconomical if produced using conventional drilling technology due to increased drilling problems and prolonged non-productive time. Seeking new ways to reduce drilling cost and minimize risks has led to the development of Managed Pressure Drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regimes. Quantitative risk assessment techniques are often used to answer these questions. Quantitative risk assessment is conducted for the various stages of drilling operations – drilling ahead, tripping operation, casing and cementing. A diagnostic model for analyzing the rotating control device, the main component of managed pressure drilling techniques, is also studied. The logic concept of Noisy-OR is explored to capture the unique relationship between casing and cementing operations in leading to well integrity failure as well as its usage to model the critical components of constant bottom-hole pressure drilling technique of managed pressure drilling during tripping operation. Relevant safety functions and inherent safety principles are utilized to improve well integrity operations. Loss function modelling approach to enable dynamic consequence analysis is adopted to study blowout risk for real-time decision making. The aggregation of the blowout loss categories, comprising: production, asset, human health, environmental response and reputation losses leads to risk estimation using dynamically determined probability of occurrence. Lastly, various sub-models developed for the stages/sub-operations of drilling operations and the consequence modelling approach are integrated for a holistic risk analysis of drilling operations

Application of Artificial Intelligence in Drilling and Completion

In this chapter, we will delve into the applications of Artificial Intelligence (AI) in drilling and completion engineering within the oil and gas industry. The scope of this chapter will include the fundamentals of machine learning and deep learning, the essential algorithms, and the workflow of AI in drilling and completion engineering, from data collection to implementation and optimization. Furthermore, we will discuss various AI application areas, such as drilling parameter optimization, downhole environment detection, intelligent completion design, and more. Lastly, we will address the challenges and prospects of AI in drilling and completion engineering, examining issues related to data quality, model accuracy, reliability, and future development trends. This comprehensive exploration aims to provide readers with a solid understanding of the potential and limitations of AI in the drilling and completion engineering domain

Land suitability assessment for wheat production using analytical hierarchy process in a semi-arid region of Central Anatolia

Rational planning of soil resources based on their capabilities are needed for the sustainable use of agricultural lands. Land suitability classification is an important evaluation tool for the management of soil resources. This study aimed to evaluate the land suitability for wheat (Triticum aestivum) cultivation using an approach that integrates multi-criteria decision making (MCDA) analysis and geographic information systems (GIS). The study area cover 21146 ha land and is located within the land consolidation area in the Çumra Plain, located in Central Anatolia of Turkey, The physical, chemical and fertility properties of the soil samples collected from 342 points in the study area were used as parameters in the wheat suitability assessment. The relative weight values of the soil parameters were determined by the Analytical Hierarchy Process (AHP). Literature and expert opinion were used in the creation of the AHP matrices and the determination of the sub-criteria. The criteria with the highest weight values or which have the highest impact on wheat growth were soil texture (0.30) and pH (0.16), while the lowest weight values were given for micro elements (0.02). Land Suitability Assessment was applied to the maps of soil variables using weighted overlay analysis in the GIS environment by using the relative weights. Thus, the suitability of the study area for wheat cultivation was mapped. The results revealed that 74% of the study area was highly suitable (S1) and 24% was moderately suitable (S2) for wheat cultivation. The coefficient of determination (R2) was 0.81, which indicated a successful prediction of the GIS-MCDA hybrid approach for wheat suitability assessment. Integration of land suitability analyzes specific to plant variety in land consolidation projects can provide a more detailed perspective on the land in the design of planning studies. © 2022 Informa UK Limited, trading as Taylor & Francis Group

A risk analysis-best worst method based model for selection of the most appropriate contract strategy for onshore drilling projects in the Iranian petroleum industry

The pre-project planning phase has a significant impact on the achievement of project objectives because during this stage, major decisions including involving contract strategies are made with a high degree of uncertainty. Studies show that the contract type can play a unique role in the achievement of project success. On the other hand, drilling projects can be considered as one of the most critical types of projects in the petroleum industry. In this research, a novel risk based best-worst method (risk-BWM) is proposed for solving the issue of selecting the best contract strategy. A three level methodology was designed; firstly, the risk breakdown structure (RBS) of drilling projects was created in four levels including one heading in level 0, eight main areas of risk in level 1, 34 sub-areas of risk in level 2, and finally, 217 risk items in level 3. Secondly and on the basis of BWM, the weights of risk factors were determined as the selection criteria and consequently the best and the worst criteria were specified. Finally, using pair-wise comparisons between six types of drilling prevalent in contracts, the most appropriate contract type was proposed. The contribution of this study is the development of a generic RBS for drilling projects and application of the risk factors for the first time for the selection of contract type using the BWM method, which has the potential of being adapted for other types of underground projects

Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations

The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation

Coastal Deposits: Environmental Implications, Mathematical Modeling and Technological Development

This Special Issue on "Coastal Deposits: Environmental Implications, Mathematical Modeling, and Technological Development" includes seven high-quality, innovative research papers dealing with many scientific aspects regarding the coast, through mathematical modelling and innovative techniques in the study and preservation of the coastline from erosion, such as coastal watch camera installations, remote sensing, the use of biocementation, or analytical techniques, to assess incompatibilities in the sustainable use of the coast, including worrying issues as pollution of the marine environment and ecosystem deterioration

Advances in Binders for Construction Materials

The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry