A framework for making maintenance decisions for oil ...

Journal of Natural Gas Science and Engineering 26 (2015) 1050e1058

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A framework for making maintenance decisions for oil and gas drilling and production equipment Yang Tang a, *, Zhengwei Zou b, Jiajia Jing c, Zhidong Zhang c, Chong Xie a a

School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China School of Computer Science, Southwest Petroleum University, Chengdu 610500, China c Safety, Environment, Quality Supervision & Testing Research Institute, CCDE, Guanghan 618000, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 January 2015 Received in revised form 23 July 2015 Accepted 24 July 2015 Available online 31 July 2015

There are few scientific maintenance decision-making methods in current maintenance and management of drilling and production equipment (DPE). Conventional methods have some conspicuous deficiencies and shortcomings, for example, unreasonable maintenance methods, surplus or insufficient maintenance, exorbitant maintenance costs and increasing failure frequency, which have caught a great influence to production safety and economic cost in the oil and gas exploitation process. In this study, a framework for making maintenance decisions was presented in order to improve the maintenance and management of the DPE. First, eight evaluation indexes and their scoring criteria were defined to quantify subjective evaluation of importance level of the DPE. Then, a linear weighted mathematical model was presented to calculate importance level value and a weight computing method of each evaluation index was put forward based on the Analytic Hierarchy Process (AHP). And the subjective effects were eliminated with Monte Carlo Simulation (MCS) in the scoring process. Next, maintenance decision-making trees (MDMTs) for the DPE were set up by reference to the logic decision tree of reliability-centred maintenance (RCM). Finally, feasibility of the framework was verified by testing a well control system in Tarim Oilfield. Therefore, the framework for making maintenance decisions can provide reasonable maintenance methods and achieve scientific maintenance and management for the DPE. © 2015 Elsevier B.V. All rights reserved.

Keywords: Importance level evaluation Analytic hierarchy process Monte Carlo simulation Maintenance decision-making Drilling and production equipment

1. Introduction The management system of point inspection and regular repair (PIRR) is regarded as the present core of maintenance and management for onshore and offshore oil field equipment, which mainly adopt corrective maintenance (CM), time-based maintenance (TBM), detection-based maintenance (DBM), etc. (Doostparast et al., 2014; Perrons and Richards, 2013). According to field investigation, we found that the overwhelming majority of major accidents and economic losses in the drilling and production process of oil and gas were caused by equipment failure and human factors. Most equipment failures were attributed to the current maintenance and management method that was backward and unscientific on the basis of the experience of the maintenance and repair personnel. The conventional maintenance strategies could not be applied by the maintenance and repair personnel to carry

* Corresponding author. E-mail address: [email protected] (Y. Tang). http://dx.doi.org/10.1016/j.jngse.2015.07.038 1875-5100/© 2015 Elsevier B.V. All rights reserved.

out timely maintenance or replacement for the DPE. Moreover, there were no effective decision-making methods or scientific theoretical models to guide the maintenance process for the DPE. Negative outcomes, including surplus repair, insufficient repair, unreasonable repair intervals, and higher maintenance costs, were brought about during maintenance and management (Eti et al., 2006; Kyriakidis and Dimitrakos, 2006). Therefore, it is necessary to establish a new framework for making maintenance decisions for the DPE using a mathematical model and decision-making theory in order to improve equipment reliability, simplify maintenance decision processes and ensure production safety. There have been some widely used maintenance method and theory in aerospace, military, rail transportation, electricity, shipbuilding and other industries (Dekker, 1996; Murthy et al., 2002). Over the years, a lot of research works on maintenance strategies and decision-making models have been done as well. Bevilacqua and Braglia (Bevilacqua et al., 2000) proposed a method to evaluate importance level of the power plant equipment based on MCS and modified FEMCA. Bertolini and Bevilacqua (Bertolini and Bevilacqua, 2006) presented a new technique to determine the

Y. Tang et al. / Journal of Natural Gas Science and Engineering 26 (2015) 1050e1058

best strategies for the maintenance of critical centrifugal pumps in an oil refinery. Based on AHP, a maintenance strategy of a multi mez de criteria classification of equipment was proposed by Go n Hijes and Cartagena (de Leo n Hijes and Cartagena, 2006), and Leo oil pipeline projects were effectively evaluated by Dey (Dey, 2004) with a multiple-attribute decision-making technique. Chang, Chang and Zio (Chang et al., 2010) applied the MCS to estimate the production availability in offshore installations. The recent research on maintenance strategies includes work by Aida and Fathi (Azizi and Fathi, 2014), who presented an empirical investigation to rank different factors of optimum maintenance strategies based on a fuzzy analytic hierarchy process, and by Arunraj and Maiti (Arunraj and Maiti, 2010), who exhibited a technique of maintenance policy selection created based on the risks of equipment failure and costs of maintenance. Both AHP and MCS that are feasible and reliable mathematical models are often used for making decisions in science management. However, very few applications of the AHP and the MCS exist for making maintenance decisions for DPE through research and investigation. We have also surveyed a few maintenance decision-making methods and models associated with DPE, especially ones that are based on different importance levels. However, the DPE is different from the equipment in other industries, such as failures types and distributions, maintenance methods and costs, and reliability and safety requirements, due to their harsh construction environments, complicated working conditions and extremely high safety requirements in the drilling and production process of oil and gas (Hmida et al., 2013; Du et al., 2013). Thus, these existing maintenance decision-making methods and maintenance strategies that are applied to the equipment in other industries are not directly suitable for the DPE. It is necessary to study a maintenance decision-making method for the DPE by focussing on the features of the drilling and production process. Therefore, we put forward a new framework for making maintenance decisions for the DPE based on their different importance levels. Through the framework for making maintenance decision in this paper, more reasonable and more effective maintenance methods can be devised for the DPE to guarantee the reliability and security of the drilling and production operation of oil and gas. The remainder of this paper is organized as follows. In Section 2, evaluation indexes and their scoring criteria for importance level of the DPE are defined. In Section 3, an evaluation model for importance level of the DPE is established using AHP and MCS approach which is applied to reduce the subjective influences in the scoring process. Then, the DPE are divided into three categories according to their different importance levels and their three MDMTs are set up based on RCM theory in Section 4. A case study for the well control system is carried out to verify the framework in Section 5. Finally, Section 6 provides some discussion and conclusions.

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should be different. So there is a relative importance level between them. The definition and the fundamental scale of the relative importance level were made in Table 1. The relative importance levels of eight evaluation indexes can be scored by reference to the Table 1. Then, we made scoring criteria of eight evaluation indexes based on reviews from maintenance engineers and operating personnel in the field investigation. The influence levels of the evaluation indexes were classified into three to five grades depending on their respective characteristics and scored with a 10-point system (Yuliang et al., 2003; Triantaphyllou et al., 1997). The definitions of the influence levels and the scoring criteria for the evaluation indexes of the DPE are as follows: 2.1. Influence of failure on personnel and environment safety (S) Equipment failure may bring about some disasters (such as poison gas leakage and diffusion, fires, and explosions) in the field during oil and gas exploration and development. These disasters will affect personnel safety and environmental pollution. However, their possible influence levels should be considered in the case of the DPE failure, and their scoring criteria are shown in Table 2. 2.2. Influence of failure on system functions (SF) When equipment failures occur, the influence on functions of the overall system should be a major concern during the drilling and production process. The definition of the influence levels and the scoring criteria of SF are given in Table 3. 2.3. Average failure rate (FR) of equipment Mean time between failures (MTBF) of the DPE can be calculated from their operational records corrected by the operators and repairers and the reliability databases for the relevant equipment. In Table 4, the numeric range of the MTBF and the scoring criteria for FR are shown. 2.4. Maintenance costs (MC) According to plenty of investigation for equipment maintenance and repair in the oil field, these factors, including structural complexity, maintenance time, spare part costs, and maintenance store location should be accounted for in the MC of the DPE. The scoring criteria for MC are shown in Table 5. 2.5. Downtime loss (DL) This evaluation index refers to the economic loss arising from downtime. The downtime attributes economic loss to equipment maintenance or replacement in oil drilling operations. The scoring criteria for DL are shown in Table 6.

2. Definition of the evaluation index and scoring criteria 2.6. Monitoring availability (MA) of equipment failure According to extensive investigation and research, we found that influence factors of importance level of the DPE could be classified into four categories: reliability factors, economy factors, monitoring availability factors and maintainability factors. These four categories were further subdivided into eight influence factors, as shown in Fig. 1 (Aven and Vinnem, 2005; Yuliang et al., 2003). These eight influence factors were regarded as evaluation indexes of importance level of the DPE. In order to determine the importance level of the DPE with mathematical methods, it is necessary to define scoring criteria to quantify influence level of each evaluation index. For different equipment or systems in the DPE, weights of the evaluation indexes

Failure monitoring availability for DPE can be assessed based on monitoring possibilities of equipment failure, monitoring complexity, monitoring cost, etc. The scoring criteria for MA are shown in Table 7. 2.7. Downtime (DT) In the maintenance and repair process of the DPE, the DT includes the time (in man-hours) for idleness, maintenance and restart. Combining with the actual situation on site, we presented the scoring criteria for DT in Table 8.

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Fig. 1. Evaluation indexes evaluating the importance level of the DPE.

Table 1 The definition and fundamental scale of the relative importance level.

Table 5 Scoring criteria for MC.

Definition

Score

S/N

Maintenance costs (US$)

Score

Equally important Moderately important Strongly important Very strongly important Extremely important Intermediately important

1 3 5 7 9 2, 4, 6, 8

1 2 3 4 5

160,000

0e2 2e4 5e6 7e9 10

Table 6 Scoring criteria for DL.

Table 2 Scoring criteria for S. S/N

Influence level

Score

1 2 3 4

No influence Slight influence Greater influence Significant influence

0e2 3e4 5e8 9e10

7000 3000e7000 1000e3000 300e1000 > < 11 u21 u1 þ ðu22 lÞu2 þ / þ u2n un ¼ 0 //// > > : un1 u1 þ un2 u2 þ / þ ðunn lÞun ¼ 0

(3)

3.2. Analysis of eliminating the subjective factors based on the MCS In this framework, the score values of the evaluation indexes were directly given by the expert or the maintenance engineer in the scoring process based on their scoring criteria. Therefore, the scoring value and the importance level of the equipment might be subject to some influence from subjective factors and individual differences among people, which would lead to some errors in the end results of the importance level of the DPE. According to the investigation and analysis of similar problems, the above problem can be solved using the MCS approach (Williams-Kovacs and Clarkson, 2014). After the weight coefficients ai were solved using the AHP, using many simulation calculations with the MCS approach for the result of the previous step will effectively eliminate the influence of subjective factors. With the application of the MCS approach and the AHP, the robustness of the importance level Table 10 Standard value of the mean random consistency index RI. n

1

2

3

4

5

6

7

8

RI

0.00

0.00

0.58

0.9

1.12

1.24

1.32

1.41

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of the DPE is further increased. The logic block diagram of the MCS is shown in Fig. 2 (Yuliang et al., 2003; Marseguerra and Zio, 2000). As shown in the Fig. 2, eight random numbers in [0, 1] were generated in the MCS process. The random numbers were regarded as the weight coefficient of the eight evaluation indexes and were assigned with the priority order obtained with the AHP in the previous step. In other words, for any group of random numbers, the largest random number is assigned to the top priority, the smallest one to the lowest priority, and the rest of the random numbers to the other evaluation indexes. Then, in an MCS computation, the total score of all of the evaluation indexes of the equipment can be calculated using Eq. (1), and the importance level of the equipment can be obtained and ranked according to the Index. Using N simulation calculations, several ranking values for the same equipment are obtained based on their different importance levels. Then, the importance level of a single piece of equipment can be determined based on its cumulative frequency sequence reaching 1; more specifically, the faster the cumulative frequency of one piece of equipment reaches “1”, the higher the importance level of that equipment (Yuliang et al., 2003).

4. Basing maintenance decisions on the importance level of the equipment According to the statistical data indicating the priority order of the equipment, their cumulative frequencies can be plotted. Based on the principle of establishing a cumulative frequency curve chart, the percentage of area on the right side of the curve can be taken as another representation of the importance level of the equipment. A larger percentage indicates a higher importance level. Based on the different area percentages of the equipment, namely, the different importance levels among them, the DPE can be divided into three categories: Class A equipment with an area percentage of 0e25%,

Class B equipment with an area percentage of 25%e65% and Class C equipment with an area percentage of 65%e100%. From field surveys and the related literature, the existing maintenance methods for DPE include lubrication (LUB), service (SVC), corrective maintenance (CM), time-based maintenance (TBM), hidden failure detection (HFD) and condition-based maintenance (CBM). To effectively implement these maintenance methods, the primary failure mode, the failure effect and the cause of the three categories of equipment are analysed before establishing the MDMTs of the DPE. Then, the MDMTs of the DPE are established by referencing the logic decision diagram of reliability centred maintenance (RCM). The MDMTs of the DPE are described in detail as follows: (1) The failure of Class A equipment has little or no influence on the function of the entire drilling and production system or causes lower maintenance costs. Increasing the spare parts inventory or decreasing the fault frequency for Class A equipment cannot affect the drilling and production process of oil and gas. Four maintenance strategies, including LUB, SVC, CM and TBM, are suitable for Class A equipment. A maintenance decision tree for Class A equipment is shown in Fig. 3 (Rausand, 1998). (2) When Class B equipment fails, it might result in more severe consequences, but it usually does not influence personnel safety or the environment. The failure frequency of Class B equipment could be reduced through reasonable maintenance strategies that might cause higher maintenance costs. An MDMT for Class B equipment with the appropriate maintenance strategies, including LUB, SVC, TBM, CM and HFD, is shown in Fig. 4 (Rausand, 1998). (3) The failure of Class C equipment might endanger personnel safety, pollute the environment or cause significant economic consequences. To ensure the operation reliability and maintenance economy of Class C equipment, the failure frequency should be reduced by increasing the maintenance costs with advanced maintenance methods. From the analysis above, a maintenance decision tree for Class C equipment is shown in Fig. 5, adopting LUB, SVC, TBM, HD and CBM (Rausand, 1998). 5. Case study The usage and maintenance records for a well control system used in drilling operations were collected from an oil company's drilling crew in the Tarim Oilfield. We performed analyses of their importance levels and maintenance methods with the framework of making maintenance decisions for the DPE. First, a hierarchy tree of the well control system was established by analysing its matching equipment, as shown in Fig. 6.

Fig. 2. Logic block diagram of the MCS.

Fig. 3. MDMT for Class A equipment.


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Fig. 4. MDMT for Class B equipment.

In order to obtain the relative importance level of the eight evaluation indexes, the operation and maintenance records of the equipment in the well control system were collected and analysed from the related resource information database. Then, the pairwise comparison among the eight evaluation indexes was evaluated by the maintenance engineer and expert. The score value of each pairwise comparison was obtained by reference to the Table 1. After determining the score values, a judgement matrix D was constructed with Eq. (2) as follows:

2

1 6 1=6 6 6 1=7 6 6 1=7 D¼6 6 1=8 6 6 1=8 6 4 1=9 1=9

6 1 1=7 1=5 1=6 1=6 1=7 1=7

7 7 1 4 3 3 2 1=2

7 5 1=4 1 1=2 1=3 1=4 1=4

8 6 1=3 2 1 1=2 1=3 1=2

8 6 1=3 3 2 1 2 1=2

9 7 1=2 4 3 1=2 1 1=4

3 9 77 7 27 7 47 7 27 7 27 7 45 1

Entering the judgement matrix D into Eq. (3) in Matlab software, its maximum characteristic value lmax was calculated:

lmax ¼ 8:9204 The eigenvector related to the maximum eigenvalue lmax was obtained with Eq. (4) as follows:

W ¼ ð 0:4696

0:2345

0:0289

0:0950

0:0627

0:0401

coefficients and priorities of eight evaluation indexes were determined for the equipment in the well control system, as shown in Table 11. Based on the logic block diagram of the MCS shown in Fig. 2, a programming calculation of the MSC was carried out by using the Matlab software. In the calculation process with the MSC, the simulation times was taken as N ¼ 2000, and a dataset that includes 2000 sequences for a single piece of equipment can be obtained. After a series of calculations on all of the well control equipment, the statistical approach was applied to process these data about the sequence of the equipment in the MSC. Finally, the cumulative frequency diagram about the sequence of the importance level of the well control equipment, including Hydraulic Accumulator, Hydraulic Pump, Oil Suction Filter and Oil Return Filter, is plotted in Fig. 7. In the Fig. 7, the cumulative frequency of both Hydraulic Accumulator and Hydraulic Pump reach “1” when the sequence of the equipment is at “8”. However, the cumulative frequency of the Hydraulic Accumulator increases faster than that of the Hydraulic Pump before reaching “0.8”, which indicates that the importance level of the Hydraulic Accumulator is higher than that of the Hydraulic Pump. The cumulative frequency of the Hydraulic Pump is faster than that of the Oil Suction Filter and the Oil Return Filter. Therefore, the sequence of the importance level is as follows:

0:0458

And the consistency of the judgement matrix D was checked using Eqs. (5) and (6):

CI ¼ 0:1315 CR ¼ 0:0923 < 0:1 The results above show that the judgement matrix D that were constructed through pairwise comparisons for eight evaluation indexes abided by the consistency demand. Therefore, the weight

0:0236 Þ

Hydraulic Accumulator, Hydraulic Pump, Oil Suction Filter and Oil Return Filter. Additionally, the percentage of area on the right side of each cumulative frequency curve can be determined based on the Fig. 7. Then their area ratios are given in the form of histograms in Fig. 8. The area ratio of the Hydraulic Accumulator is between 65% and 100% in the Fig. 8, so it is sorted as Class C equipment. The Hydraulic Accumulator should adopt HD and CBM based on the MDMT for Class C equipment in the Fig. 5. The Hydraulic Pump is also Class C equipment from its area ratio, which is higher than 65% in the Fig. 8. According to the MDMT in the Fig. 5, HD and TBM were applied to the Hydraulic Pump. The Oil Suction Filter belongs to Class B

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Fig. 5. MDMT for Class C equipment.

Fig. 6. Hierarchy tree of the well control system.

equipment due to its area ratio falling within the range of 25%e65%. From the MDMT for Class B equipment in the Fig. 4, the Oil Suction Filter should be implemented with the HD and CM maintenance methods. The area ratio of the Oil Return Filter is less than 25%, so it is Class A equipment. Its maintenance method should be SVC and CM, referring to the MDMT in the Fig. 3. Finally, the maintenance strategy and method for the Hydraulic Accumulator, the Hydraulic Pump, the Oil Suction Filter and the Oil Return Filter were formulated and performed. Through the reviews of the relevant experts and maintenance engineers for the maintenance methods of the above equipment, the framework of the maintenance decision making of the DPE is considered reasonable and effective.

6. Conclusion Due to the previous maintenance decision-making process Table 11 Weight coefficients and priorities of the eight evaluation indexes. S/N

Factor

Weight

Priority

1 2 3 4 5 6 7 8

S SF FR MC DL MA DT M

0.4696 0.2345 0.0289 0.0950 0.0627 0.0401 0.0458 0.0236

1 2 7 3 4 6 5 8

without using mathematical model and decision-making theory, there were some undesirable phenomena and problems during the maintenance and management for oil and gas DPE. In order to improve the situation of the maintenance and management, we presented a framework for making maintenance decisions for DPE in this study to restrain safety accidents and economic losses in the oil and gas exploitation process. We summarized four categories influence factors, including eight influence factors to evaluate the importance level of the DPE. Eight influence factors were regarded as evaluation indexes and their scoring criteria were defined to quantify the result of subjective evaluation. The evaluation model calculating importance level of the DPE was established based on the AHP and the MCS. By applying the model, not only the importance levels of the DPE were obtained, but also the human subjective effects of the scoring process were eliminated. Then, we divided the DPE into three categories, including Class A, B and C based on their importance level values and established their MDMTs by reference to the logic decision tree of RCM theory. Finally, a well control system in the Tarim Oilfield was made maintenance decisions according to the framework. Their maintenance methods from the MDMTs were considered reasonable and effective by the relevant experts and maintenance engineers. So the feasibility of maintenance decision-making framework is verified effectively. In this study, the mathematical model and decision-making theory applying in the framework for making maintenance decisions improve scientificity and reasonability of the maintenance and management for the DPE. On the premise that the


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Fig. 7. Cumulative frequency sequences of the equipment.

Fig. 8. Area percentage of the equipment.

evaluation indexes and scoring criteria are revised according to their industry standards and maintenance features, the framework can be popularized not only in the petroleum and petrochemical equipment but also in other industries equipment. There are three evaluation methods of equipment importance level that were presented in this paper. They can be applied to more complicated equipment or systems in other industries to identify key components and parts and simplify the analysis tasks in next step. Moreover, these research methods and results in this study will probably provide some references for mechanical integrity management in the petroleum industry. But some relevant topics have not yet been completely worked out in our study, for example, optimizing inspection and maintenance intervals and reducing maintenance costs. In our next research work, we will focus on these topics to improve the framework further. On account of the framework possessing fixed procedures and contents in application process, we are also planning to develop application software so that its usability and effectiveness will be improved in engineering applications.

Conflict of interests The authors declare that there are no conflicts of interest regarding the publication of this article. Acknowledgements The work was supported by the Natural Science Foundation of China (Grant no.51274171) and the Graduate Student Innovation Fund of the School of Mechatronic Engineering, Southwest Petroleum University (CX2014BZ04). References Arunraj, N.S., Maiti, J., 2010. Risk-based maintenance policy selection using AHP and goal programming. Saf. Sci. 48 (2), 238e247. Aven, T., Vinnem, J.E., 2005. On the use of risk acceptance criteria in the offshore oil and gas industry. Reliab. Eng. Syst. Saf. 90 (1), 15e24. Azizi, A., Fathi, K., 2014. Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Manag. Sci. Lett. 4 (5), 893e898.

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