An integrated method of human error likelihood assessment for shale-gas fracturing operations based on SPA and UAHP
Introduction
Hydraulic fracturing is a significant technique used to improve the production of shale-gas resources, and has become increasingly popular in drilling industries (Rahm, 2011; Wang et al., 2018). It is a highly industrialized process involving multiple equipment, substances, and operational stages. As shown in Fig. 1, there are different kinds of fracturing equipment including various types of trucks: fracturing, blender, coiler, manifold, monitor, sand storage, water storage, and chemical storage. Typical fracturing operations also require fracturing fluid (i.e., water and chemicals) and fracturing proppant (i.e., sand). In addition, a life-cycle fracturing process comprises three major stages: pre-fracturing, fracturing, and post-fracturing. The first stage mainly includes equipment placement and connection, start-up checks, pumping and evacuation, pressure testing, and perforation. The second stage also consists of many sub-stages, such as fluid displacement, packer sealing, fluid extrusion, sand addition, displacement and extrusion, and pump and well shutdown. Lastly, the post-fracturing stage encompasses fluid purging, equipment disassembly, truck evacuation, and fluid flow-back.
Fracturing operations entail extremely hazardous work involving high-pressure chemicals, heavy equipment, and flammable gases (Engelder and Zevenbergen, 2018). Human errors frequently result in sand blockages, equipment damage, and fracturing fluid leakage, which can cause serious water pollution, major casualties, and heavy equipment accidents. Consequently, high-risk events can trigger a domino effect and cause the abandonment of entire shale-gas wells.
The US Environmental Protection Agency (U. S. Environmental Protection Agency, 2010) suggested that an assessment of the relative influences of human errors would contribute to improving the overall safety of shale-gas fracturing. The U. S. Environmental Protection Agency, (2015) and Clancy et al. (2018) further investigated the potential causes of produced water spills and found that most of them were caused by human errors. In addition, Time magazine (US) reported that a blowout of the Chesapeake Energy gas well in northeastern Pennsylvania that resulted in the spillage of gallons of fracturing fluid on the surrounding ground; a subsequent accident survey indicated one of the main reasons for the blowout to be human error (Walsh et al., 2011). Finally, Heinberg et al., (2014) concluded that European fracturing companies seeking to improve operational safety should gain experience in the elimination of human errors from North American industry.
The shale-gas fracturing industry in China is facing huge challenges as it is still in the start-up and growth stages. Firstly, most of the operators are chosen from conventional oil and gas well fracturing teams but only receive insufficiently basic, short-term training prior to commencing in their roles. This results in a severe shortage of operational experience, professional knowledge, and safety skills, with human errors frequently occurring over a whole fracturing period. Secondly, shale-gas wells are mostly located in the mountainous areas of southeast China and are characterized by having small areas, requiring the use of hazardous materials, and harsh environments (Zhai et al., 2012; Dong et al., 2012; Li et al., 2013). Additionally, fracturing operations are monotonous, of long duration, and require high physical strength. Due to the challenging operating conditions, fracturing operators often suffer psychological and physical fatigue, which significantly increase the likelihood of human errors. Finally, different types of human errors, such as partial observations, incorrect judgment, and nonscientific decisions, frequently occur during the fracturing process; the latent, unpredictable, and various natures of human errors make them difficult to study. An assessment of human error likelihood is required to prevent accidents and ensure the overall safety and stability of fracturing operations.
There is a rich breadth of published studies associated with human reliability analysis or human error likelihood assessment in oil and gas industry. One research group led by Dr. Faisal Khan proposed some methods for offshore / marine operations, facilities, systems, emergencies, or evacuations, such as Success Likelihood Index Method (SLIM) (DiMattia et al., 2005; Khan et al., 2006; Abbassi et al., 2015; Islam et al., 2017a), Human Error Assessment and Reduction Technique (HEART) (Noroozi et al., 2014a, 2014b; Islam et al., 2017b), Technique of Human Error Rate Prediction (THERP) (Abbassi et al., 2015), Bayesian Network (BN) (Musharraf et al., 2013; Islam et al., 2018a, 2018b), Risk Matrix (RM) (Deacon et al., 2010, 2013), and so on. Another research group led by Dr. Emre Akyuz also developed new techniques for offshore units, marine accidents, as well as maritime ships and tankers, including Human Factor Analysis and Classification System (HFACS) (Akyuz and Celik, 2014), ANP-based HFACS (Akyuz, 2017), Cognitive Reliability and Error Analysis Method (CREAM) (Akyuz and Celik, 2015a; Akyuz, 2015), HEART (Akyuz and Celik, 2015b), AHP-based HEART (Akyuz and Celik, 2015c, 2016a), fuzzy HEART (Akyuz and Celik, 2016b), and fuzzy SLIM (Akyuz, 2016). Similarly, other studies used these methodologies. Ung (2015, 2018) firstly put forward a new fuzzy CREAM model for maritime human reliability analysis, and then integrated three conventional methods – fuzzy CREAM, BN, and fault tree analysis – for human error assessment in oil tanker grounding. Based on BN, Strand and Lundteigen (2016, 2017) incorporated human factors modelling and human-machine interface in human reliability analysis of offshore drilling operations. Cai et al. (2013) applied dynamic BNs to assess human factor barrier failure on offshore blowouts, where human factors were classified into individual, organization, and group. Using fuzzy HEART, Kumar et al. (2017) developed an approach to quantify human error probabilities and selected an LPG refueling station to illustrate its validity. Zhou et al. (2017) studied human factor risks in oil and gas drilling industries wherein human errors were identified in detail and their likelihoods and severity levels were calculated to establish their RM. Theophilus et al. (2017) presented a novel HFACS that was suitable for catastrophic oil and gas accidents. In sum, only fuzzy HEART, fuzzy SLIM, fuzzy CREAM, and BN considered the uncertainties.
However, due to the latency, unpredictability, and variety of human errors in fracturing operations, these existing studies are not adequate as they do not overcome epistemic uncertainties (i.e., fuzziness, grayness, and incompleteness). In comparison, Set Pair Analysis (SPA) has two main advantages. It can comprehensively address epistemic uncertainties from assessed objects, assessment procedures, assessor subjectivities, and incomplete data (Zhao, 1992; Zhou and Zhang, 2013); it is considered as the combination of certainties and uncertainties because it uses the identity-discrepancy-contrary model, which is more applicable to decision-making and practical conditions (Li et al., 2016; Yan and Xu, 2018). Furthermore, SPA can provide the static states and dynamic trends of the assessed objects to decision-makers (Yue et al., 2014; Chong et al., 2017). Such results contribute to the reduction of high-risk factors and can be incorporated in operational guidelines and safety checklists. At present, SPA has been widely applied in many fields, such as environmental pollutions (Yue et al., 2014; Yang et al., 2014; Li et al., 2016), occupational hazards (Chong et al., 2017), and facility failures (Zhang and Zhang, 2010; Wang et al., 2015; Yan and Xu, 2018). Particularly in oil and gas industries, some studies introduced the model of SPA to safety management of oil depot (Zheng and Chen, 2006), risk evaluation of urban gas pipeline system (Zhou and Zhang, 2013), failure possibility assessment of LNG terminal (Qi et al., 2014), and failure probability analysis of submarine pipeline (Shu et al., 2017). Hence SPA is selected as the basic technique of human error likelihood assessment in this paper.
The top priority in SPA is how to establish assessment indexes. Current studies mostly apply performance shaping factors (PSFs) and performance influencing factors (PIFs) for human error analysis (Kim and Jung, 2003; Dalijono et al., 2006; Landucci and Paltrinieri, 2016; Rasmussen and Laumann, 2018). However, these two methods can be assessed by relying on expert opinions. As there is currently little available information on human errors in shale-gas fracturing operations, it is challenging to use PSFs and PIFs in this paper. 4 M theory is one of the typical accident causation theories in systems engineering (Li, 2009; Yuan, 2015). From a system-wide aspect, it can reasonably, comprehensively, and effectively analyze human errors in the complex fracturing process. It is also clear and concise, and accessible to professionals or laymen. Therefore, the indexes of human error likelihood assessment are established based on the 4 M theory for SPA.
Traditionally, the SPA weight matrix is established based on Analytic Hierarchy Process (AHP) (Zheng and Chen, 2006; Zhou and Zhang, 2013; Chong et al., 2017). AHP only quantifies the uncertainties using a certain number from one to nine. However, assessors cannot accurately make pairwise comparisons for assessment indexes due to the uncertainties of human errors in fracturing operations. Therefore, the Uncertain Analytic Hierarchy Process (UAHP) is applied for weights optimization using interval numbers to reduce aleatory uncertainties and improve the accuracy of index weights (Wei et al., 1994; Wu and Zhu, 2002). In addition, due to the inconvenience of interval weights, SPA is specifically used to turn them into crisp values.
Therefore, this paper proposes an integrated method of human error likelihood assessment for shale-gas fracturing operations based on a UAHP-SPA model. The rest of the paper is organized as follows. Section 2 details the proposed method using basic theories, including 4M theory, SPA, and SPA-based UAHP. A case study of an on-site fracturing operator is selected to illustrate the method in Section 3, where a comparison to the Fuzzy Comprehensive Evaluation Method (FCEM) is also provided. Finally, conclusions are made in Section 4.
Section snippets
Methodologies
There are many human factors that can lead to errors, incidents, and accidents. To assess the human error likelihood, an integrated method is proposed based on a novel UAHP-SPA model (see Fig. 2). It consists of a set of comprehensive assessment indexes, the UAHP weight optimization model, and the uncertainty-based assessment SPA technique.
Background
Shale-gas wells are located in mountainous areas, where fracturing operators face harsh conditions and physically-taxing operations. Consequently, there are serious human errors due to the psychological and physical fatigue. A case is described as follows: the pump pressure sharply declined at the last stage of hydraulic fracturing around 4:00 am due to a fatigued pump operator forgetting to close the butterfly valve. As a result, air came into the plunger and the pump failed.
In detail, the
Conclusions
Shale-gas fracturing requires an effective method to reduce human errors and ensure its safe operation. There have been many applications of human error likelihood assessment in oil and gas industry. However, the existing methods inadequately address the uncertainties from latent, unpredictable, and various human errors in fracturing operations. Using the UAHP-SPA model, an integrated assessment method has been proposed to improve on the existing studies. The assessment procedures have been
Acknowledgements
The authors greatly appreciate the guidance of Professor Carol Smidts and Dr. Yunfei Zhao during Qianlin’s academic visit at the Ohio State University. This work is supported by National Key Research and Development Program of China (2017YFC0805801), National Natural Science Foundation of China (No.51574263), Beijing Nova Program (Z181100006218048), and China Scholarship Council([2017]3109).
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