Gas and Oil Reliability Engineering

Modeling and Analysis

Eduardo Calixto

• Publisher: Gulf Professional Publishing
• Year: 2012
• ISBN:
  9780123919151
• Topic: Physical Sciences
• Subtopic: Energy

The advent of reliability engineering tools coupled with the cost of oil and gas operations has changed the paradigm of maintenance technology. A simple strategy of efficient replacement of failed equipment/component has been transformed into a more complex but proactive approach for keeping equipment running at peak efficiency concept of "total process" reliability engineering and maintenance. Applied Oil and Gas Reliability Engineering: Modeling and Analysis is the first book to apply reliability value improvement practices and process enterprises lifecycle analysis to the Oil and gas Industry. With this book in hand, engineers also gain a powerful guide to the most commonly used software modeling tools which aid in the planning and execution of an effective maintenance program.Easy to understand, the book identifies equipment and procedural problems inherent to oil and gas operations then applied a systematic approach for solving them. In this book, the author combines qualitative and quantitative methods with powerful software modeling tools to assist engineers in formulating a custom maintenance policy which will ensure process efficiency, reduce projects cost, reduce redundancies and optimum equipment replacement time.

• Mathematic methods for analyzing failure historical data
• Instruction for utilizing modeling systems such as MAROS, TARO, and BLOCKSIM and interpret results
• Step by Step approach for formulating an cost effective maintenance program
• Identifies equipment and procedural problems inherent to oil and gas operations
• Easily understood methods and software tools that will save time and money
• Provides a tutorial for using the most used software programs such as: MAROS, TARO, and BLOCKSIM
• Step by step instruction to create a custom maintenance policy
• Reduce project cost, reduce redundancies and optimize equipment life

Chapter 1

Life Cycle Analysis

1.1 Quantitative Failure data Analysis

Reliability is the probability that a piece of equipment, product, or service will be successful for a specific amount of time. To define the reliability of a piece of equipment, product, or service, it is necessary to collect historical failure data.

Therefore, the first step in life cycle analysis is to understand how failures occur over time and to define failure rate, reliability, availability, and mean time to failure (MTTF) to best time inspections and maintenance and to see if equipment is achieving reliability.

To conduct life cycle analysis it is necessary to have historical data about failure modes. The failure mode is the way a piece of equipment or product loses part or total capacity to conduct its function.

Many companies in the oil and gas industry and other industries do not have historical data for their equipment, and some equipment suppliers have no historical failure data for their products. Therefore, the first step in reliability applications is to collect data, but in many cases the engineer who needs the data for life cycle analysis is not the same person who fixes or performs maintenance on the equipment and collects the data. The main point is that some companies have historical data and others do not.

An environment for assessing root cause analysis and solving problems as well as making decisions based on reliable information makes the data collection process and the creation of historical data reports very important.

For companies that do not have data to make decisions, the first step is creating historical data reports before carrying out life cycle analysis. When doing so, managers must be aware of the importance of collecting equipment failure data and also instructing and supporting employees to do so. Moreover, employees must be trained in collecting data and making decisions based on reliable data. This is a big challenge for most companies, because even when procedures and programs are established, it is necessary to collect, assess, and store failure data in files and reports for access later.

Depending on the system, collecting failure data depends on maintenance and inspection routines, and this data collection process often competes with other activities. In the oil and gas industry, equipment generally does not have a high frequency of failure, which enables employees to more easily collect and work with equipment failure data.

For many reliability professionals historical failure data means a reliability index, which includes failure rate, reliability, availability efficiency, MTTF, or PDF (probability density function) parameters. For inspection and maintenance professionals, historical failure data means files with services described by type of failure of occurrence, time to repair, data, and recommendations. In fact, if there are no reliability index and PDF parameters for conducting reliability analysis, this data must be created by reliability specialists based on available data. In reality, creating the data is the first step of life cycle analysis, and then defining the reliability index based on this data. The best scenario, of course, is that the reliability index and PDF parameters are available for reliability professionals, but this is not usually the case.

Thus, two points of view among reliability professionals are discussed all over the world: the reliability index and PDF parameters must be defined in a report to make analysis easier, or index and PDF parameters must be calculated and updated for specialists. When reliability professionals assess PDF parameters from reports, the chance of error is greater than when comparing them with defined parameters based on historical data. Despite the time required to assess files creating historical data reports before and create the PDF parameters and then reliability index, life cycle analysis based on historical data and failure root are more reliable because they are better understood and updated more frequently.

Another important point is that equipment PDF characteristics change over time and PDFs must be assessed whenever a failure occurs, even though there’s a reliability index. Thus, the failure data reports must be updated from time to time. Additionally, new equipment has different life cycles over time, and this information needs to be updated, which makes the reliability index cumbersome.

To conduct life cycle analysis the following data, classified by configuration, is required:

• Individual or grouped data

• Complete data

• Right suspension data

• Left suspension data

• Interval data

Individual data is data from one piece of equipment only and grouped historical data comes from more than one piece of similar equipment. In the first case, the main objective is to assess equipment for life cycle analysis and historical failure data from one piece of equipment is enough, but such equipment should have a certain quantity of data for reliable life cycle analysis. In some cases there’s not enough historical data and it is necessary to look at a similar piece of equipment with a similar function and operational condition to create the historical failure data. In real life it’s not always easy to find similar equipment, because in many cases maintenance, operational, and process conditions interfere on the equipment life cycle. In cases where reliability analysis is conducted during the project phase, similarity is easier to obtain because operational conditions, processes, and maintenance procedures are similar to project requirements. However, to increase the reliability of life cycle analysis, historical grouped data must be used, and in this case requires considering more than one piece of similar equipment to create PDFs for th...