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In drilling industries, hydraulic fracturing of unconventional shale-gas wells is a highly industrialized process involving multiple equipment, substances, and operational stages. However, fracturing operations also involve extremely hazardous work relating to high-pressure chemicals, heavy equipment, and flammable gases. During the fracturing process, human error frequently causes sand blockages, equipment damage, and fracturing fluid leakage. An assessment of human error likelihood is essential for the prevention of resulting incidents, and thereby improving the safety and stability of the entire shale-gas fracturing operations. However, due to the latency, unpredictability, and variety of human errors in fracturing operations, previous studies have not sufficiently considered uncertainties. Hence, an integrated method that applies the UAHP-SPA model is proposed to assess human error likelihood in the fracturing process. The indexes of human error likelihood assessment in fracturing operations are first established based on 4M theory. Then, the Set Pair Analysis (SPA) technique is used to consider epistemic uncertainties from assessed objects, assessment procedures, assessor subjectivities, and incomplete data. Furthermore, the SPA-based Uncertainty Analytic Hierarchy Process (UAHP) is applied for reducing aleatory uncertainties in the index weight optimization. The proposed method is able to effectively provide static likelihood states and dynamic likelihood trends of various human errors. To illustrate its validity, an on-site fracturing operator is selected as a test case. Results show that this UAHP-SPA model is more accurate and practical compared to conventional approaches.

Leakage of high-pressure sour gas wells is one of many challenges for offshore drilling operations, which may cause serious consequences due to poisonous H2S gas diffusion in the platform with limited working space. This study presents a new method for dynamic risk analysis of H2S leakage in such sour gas fields during managed pressure drilling phases. This method can model the influence of uncertainty from accident probability and consequences, being reflected in failure rates and unmodeled factors. The accident cause-consequence analysis via BT modeling for H2S release is conducted, integrating dynamic characteristics with probability estimation based on the inference of dynamic Bayesian networks (DBNs). The individual risk under different consequence scenarios is performed by the DBN modeling as well as death probability prediction at key monitoring points dynamically. A case study focused on specific Chinese offshore wells is analyzed to demonstrate the feasibility of the proposed method. The results show that the vulnerable factors with higher values are worth being addressed for prevention. In addition, the tolerable duration in total risk with the upper bound is approximated from 4.5 to 15 min between individual risk values of 1.0E − 4 and 1.0E − 6, as well as the exposure time after 15 min deserves more attention in risk emergency management.

A pump injection system used in the shale-gas well fracturing process is subjected to various adverse factors during its service, such as high pressures of up to 105 MPa and a large displacement, leading to a high failure rate and a rapid degradation in system performance. To ensure the safety and reliability of such a system, a comprehensive safety management method based on a dynamic object-oriented Bayesian network (DOOBN) is proposed in this article. The approach provides a framework that integrates a system function model, causal model, system behaviour model, and online fault diagnosis model with a remaining life prediction model, to characterise the behaviours in a complex system, such as fault propagation and system degradation. This method could achieve fault diagnosis and also predict the degradation trend of critical components and system performance in the long term, starting from the current system state. The application of the integrated safety management approach to the specific example of the pump injection system demonstrates how each phase of the presented method contributes to the achievement of fault diagnosis and residual life prediction in a systematic and holistic way. It is shown that the proposed model is a reasonable starting point for forecasting the remaining life of pump injection systems. This approach could be integrated into a real-time safety warning device for field application.

When a disturbance occurs in a complex large-scale system, it may affect downstream equipment and several other process variables to evolve into a larger risk. The connectivity of process equipment may make it difficult to identify the propagation path of the disturbance. Understandably, the root cause identification of widespread disturbances gets its share of attention from researchers for remedial action but the prediction of the propagation path to prevent widespread disturbance is often overlooked. The scale-reasoning based risk propagation analysis method is proposed in this paper to predict the probable propagation path so that corrective actions can be taken in time to avoid further loss. By dividing the spatial scale of a complex production system, the approach uses transfer entropy to find the causal relationship between process variables and establish the risk propagation scale-reasoning model in the form of a causal map; then the risk propagation searching method, based on kernel extreme learning machine, is used to forecast the risk propagation path. An actual industrial case is analyzed to illustrate the effectiveness of the proposed method.

With the increasing development of hydraulic fracturing technologies, shale gas exploitation is becoming a highly industrial process. Casing-failure incidents which could cause serious leakage of the high-pressure and hazardous chemicals have triggered an intense public discussion in the fracturing industry. Quantitative risk analysis (QRA) is a common technique used to study the carrying capacity of a casing. However, this technique tends to only show static risk state after the casing has been run down to the formation, but is not sufficient to monitor its real-time failure risk during the whole fracturing process. Therefore, a matrix-based risk assessment method is proposed to improve the conventional QRA by using stress-strength interference theory and value function modelling to calculate the static and dynamic failure probabilities of casings, respectively, over a period of multi-stage sand fracturing. Further studies are developed to integrate these two probabilities with the application of a design matrix, particularly for the quantitative analysis and assessment of casing failures. The visual risk graphs are also provided to show the failure risk states and levels for the casings in real time. The assessment procedures can clearly delineate the operation characteristics of shale gas fracturing − high pressure, large displacement, and sand erosion. To illustrate the validity of the methodology, a production casing of a gas well at one fracturing stage is chosen as a test case. Results show that the real-time risk is more accurate and practical, as well as improving the assessment effectiveness of casing-failure incidents during a whole fracturing period.

Detecting down-hole incidents in the shale-gas well fracturing process plays an important role in ensuring that the fracturing operations are carried out smoothly. This paper proposes a method to monitor down-hole incidents by extracting the qualitative trend of process variables (QTPV) using qualitative trend analysis. This is based on the consideration that QTPV is similar at different magnitudes of down-hole incidents and that deviations from the normal pattern may indicate a possible incident. Based on this, this paper presents a real-time diagnosis and alarm method of down-hole incidents using a multi-class support vector machine ( MCSVM ) model for qualitative trend classification in real-time. Compared with the traditional modelling process in which process data is directly used as the input item to develop the MCSVM classifier, the proposed method can achieve higher global accuracy, as well as lower false and missing alarm rates, even with limited incident cases. Moreover, successful real-time diagnosis and alarm of down-hole incidents (cracks forming in the strata, channelling near the wellbore area, and sand plugs) are demonstrated. The results suggest that the presented method is a reasonable starting point for monitoring down-hole incidents during the shale-gas well fracturing process. This approach can be integrated into a real-time monitoring and alarm device for field application during fracturing operations.

Noise in pipeline pressure signal is a small noise of which the amplitude is much smaller than that of signal. But it affects recognition of pressure drop and results in inaccurate leakage localization or false alarm. Thus, a small noise reduction method based on empirical mode decomposition (SNR-EMD) is proposed to reduce the noise in pipeline pressure signal. SNR-EMD removes the noise considered as small fluctuations in signal around the mean line calculated by signal’s upper envelope and lower envelope. Meanwhile, end effect of SNR-EMD is restrained by extrema mirror extension (EME). Then tested by pressure signal in field, SNR-EMD can reduce small noise well through making the denoised signal smooth and conserving the mutation characteristics of the original signal. Finally, negative pressure wave (NPW) combined with SNR-EMD is used to locate pipeline leakage. The case study indicates that pressure drop can be well recognized and leakage can be accurately located.

The Bhopal disaster was a gas leak incident in India, considered the world's worst industrial disaster happened around process facilities. Nowadays the process facilities in petrochemical industries have becoming increasingly large and automatic. There are many risk factors with complex relationships among them. Unfortunately, some operators have poor access to abnormal situation management experience due to the lack of knowledge. However these interdependencies are seldom accounted for in current risk and safety analyses, which also belonged to the main factor causing Bhopal tragedy. Fault propagation behavior of process system is studied in this paper, and a dynamic Bayesian network based framework for root cause reasoning is proposed to deal with abnormal situation. It will help operators to fully understand the relationships among all the risk factors, identify the causes that lead to the abnormal situations, and consider all available safety measures to cope with the situation. Examples from a case study for process facilities are included to illustrate the effectiveness of the proposed approach. It also provides a method to help us do things better in the future and to make sure that another such terrible accident never happens again.

Gas leakage from pipeline leads to significant environmental damages and industrial hazards, so small leakage detection for gas pipeline is essential to avoid these serious leakages. However, because of the high frequency component of leakage signal attenuates quickly, traditional detection method which inspects pressure or vibration signal has problem to get effective information from leakage signal. So, a novel detection method based on acoustic wave is proposed. This paper, firstly, researches on the phonation principle of pipeline leakage and the characteristic of sound source, and simulates the leakage acoustic field on the basis of aero acoustics. Secondly, using Wavelet Packet Transform method and Fuzzy Support Vector Machine pattern classification, the laboratory testing for identifying acoustic signal of gas pipeline leakage is presented. Finally, the field application demonstrates that the detection system could identify small gas leakage effectively and avoids false-alarms which caused by running conditions with a good prospect.

In complex industrial system, such as gathering system, the high complex failure coupling relation among separate production process sections, personnel operation and equipment leads to a high complex potential hazard, which induces huge economic losses, environmental contamination, or human injuries. In order to insure system intrinsic safety and simplify failure mode analysis, this study proposes a novel failure mode analysis model (NFMA). NFMA is developed based on Multilevel Flow Modeling (MFM) and Hazard Operability Study (HAZOP). A graphical MFM model is introduced in NFMA by decomposing goals, functions and components, to descript flows of mass and energy of process system as basis of this model. According to the MFM reasoning rules, HAZOP investigates function nodes and deviations to identify the failure modes. Finally, the benefits and feasibility of NFMA are investigated with a case study of gathering system.

Predictive maintenance (PdM) focuses on failure prediction in order to prevent failure in advance and offer sufficient information to improve inherent safety and maintenance planning. A novel opportunistic predictive maintenance-decision (OPM) method integrating of machinery prognostic and opportunistic maintenance model is proposed in this paper to indicate the optimal maintenance time with minimal cost and safety constrains. DBN-HAZOP model quantifies hazard and operability analysis by dynamic Bayesian networks to provide prospective degradation trends of each component and the overall system for maintenance decision making. It is developed by integrating the prior knowledge of the interactions and dependencies among components and also the external environment, while the online condition monitoring data which is further to update the parameters of the model. Based on the future degradation trends given by DBN-HAZOP model, a local optimal proactive maintenance practice can be determined for each component by minimizing the expected maintenance cost per time unit. Understanding that for a complex system, whenever one of the components stops to perform a predictive maintenance action, the whole complex system must be stopped, at this moment, PdM opportunities arise for the other degraded components in the system at a reduced additional cost. Therefore, this paper further proposes an opportunistic PdM strategy for global cost optimization of predictive maintenance for the whole system, which considers failure probabilities, repair costs, down time cost and set-up cost. Case studies are given throughout to show how this approach works, and the sensitivity of the results to some of the driving cost parameters has also been examined.