Digitalization of Oil and Gas Facilities Reduces Cost and Improves Maintenance Operations

You have access to this full article to experience the outstanding content available to SPE members and JPT subscribers.

To ensure continued access to JPT's content, please Sign In, JOIN SPE, or Subscribe to JPT

The digitalization of oil and gas facilities is becoming a new technical arena. Effective solutions can be used to convert data into information and knowledge, which can then be used to improve maintenance operations. This paper discusses several aspects of this process, ranging from a discussion of maintenance strategies to the opportunities presented by extracting new information from big data.

Introduction

Fieldbuses, device diagnostics, and advanced management-and-control systems collect large amounts of data, but acquiring and applying methods of exploiting the data have lagged. End users have expressed doubts that they are realizing value from these solutions and have wondered whether a return to simpler systems is needed. In this paper, the authors conclude that condition-based maintenance can reduce the cost of maintenance operations significantly and that further potential in predictive-maintenance regimes exists as experience with base data is gained.

Reactive, Scheduled, and Condition-Based Maintenance Strategies

A reactive, or break-and-fix, type of maintenance strategy will often present the lowest maintenance operation cost, seen in isolation. But also implied is the cost of production unavailability, the safety risk posed by very hazardous events, and the risk of high repair costs after catastrophic equipment failure.

Scheduled or periodic maintenance requires one to estimate failure modes and consequences for all equipment in the plant; then, on the basis of the equipment’s expected lifetime, one calculates inspection intervals and replacement cycles. Because this is often impractical to perform for each individual piece of equipment, equipment is divided into classes depending on type and is given maintenance that is based on that type and its criticality. This approach can have some undesirable effects, including the following:

  • The 80:20 rule may apply; it states that 80% of maintenance intervention is caused by 20% of equipment.
  • A conservative approach that prioritizes avoiding operational failures will lead to short inspection intervals and high maintenance costs. 
  • Many sources conclude that human error is the source of as much as 50% of all maintenance issues. Thus, an excessive maintenance intensity will by itself lead to additional failures and the need for even more maintenance.

Thus, a transition to condition-based maintenance is desirable. For this form of maintenance to be possible, several conditions must be satisfied:

  • The fault condition must be detectable (i.e., one must have a fault progression that allows detection with sufficient time to allow corrective action).
  • The equipment must have sufficient instrumentation to allow parameters to be observed.
  • A model of the equipment must exist that can collect information about fault progression and measured data and form a sufficiently accurate “digital twin” to assess equipment condition.
  • A maintenance strategy and procedure must be implemented that can take advantage of this information.

Reliability-Centered Maintenance (RCM)

RCM is both a process and a way of defining maintenance needs. The process itself is well-documented and -tested; the quality and granularity of the RCM process are important features. Central to the RCM process are seven questions that are discussed elsewhere in the literature. This paper will concentrate on the sixth of these questions: Can we predict or prevent failure (i.e., what proactive tasks need to be performed)?

This question is the rationale behind the approach of condition monitoring—specifically, online condition monitoring. If one can predict failure by monitoring proactive tasks (inspections or other maintenance actions), then prediction can be handled by online monitoring systems. It is important, however, that the activities are not necessarily canceled; they may instead be replaced with monitoring and intended repair tasks. If a failure can be detected and a condition-monitoring system is to be used, a further detailing of information is needed:

  • What is the P–F interval [defined as the time between detection of potential failure (P) and occurrence of functional failure (F)]?
  • Is the interval relatively consistent?
  • Can the intended tasks be performed at intervals shorter than the P–F interval?

Unfortunately, for most upstream oil and gas assets, detailed maintenance planning for new assets is begun after the design and instrumentation levels have been set. This usually leads to a maintenance plan that fails to take into account technological developments that are already field-proved in other industries.

Digitalization and Maintenance

In the upstream industry, integrated operations (IO) is a generic term used to describe new ways of working facilitated by emerging information and communication technology. Depending on the company and its location, the same concept might be known as the “digital oilfield,” the “e-field,” the “smart field,” the “i-field,” “integrated asset management,” or, finally, “intelligent energy.” As this proliferation of names suggests, IO has gained wide international distribution during the last decade, and the recent work processes and organizational structures enabled by IO have spread to the fields of operational strategy and organizational management.

A common mistake in implementing an IO plan is overlooking the need to change work processes. The same problem applies when moving to a condition-based maintenance regime or when introducing new technologies to enable a digital transformation. One must begin by considering which interactions between technologies, people, and organizations one wishes to see, then one must tailor work processes accordingly.

Many companies find that lean thinking and lean processes are key to enabling more-efficient work processes and ensuring that the potential of new digital technologies is fully exploited. Lean thinking goes hand-in-hand with digital transformation, with its focus on empowering people to solve problems, delivering value to customers, and striving for continuous improvement.

Approximately 40% of production loss is related to preventable operator errors. In a normal plant, this could account for 1–2% of total plant production capacity. With an IO plan implemented, reduction of this percentage would result from reduced operator fatigue, improved automation, and effective decision support. In normal situations, even fractional improvements in plant uptime are by far the largest contributor to improved earnings.

The second IO contribution to plant uptime can come from elimination of unplanned shutdowns resulting from equipment malfunction or failure. ­Condition-based-maintenance systems assist the operator in correctly responding to possible malfunction by shifting to redundant equipment, thereby reducing the load on primary equipment. In such plants, failures can be reduced by more than 90% and an additional 2 to 3% can be added to plant availability.

Far more critical than production losses are critical safety incidents. These occur if operators fail to respond correctly to critical events or systems or are not available to do so. Critical events are often the result of multiple safety issues, each noncritical in itself, developing into unmanageable situations that can result in the complete loss of a facility and severe environmental damage.

Transformation to a Predictive-Maintenance Regime

A structured process is needed to enable the transformation to an improved maintenance regime. The following steps describe a staged plan for making this transition. The plan, described in detail in the complete paper, has been developed on the basis of the experience of early adopters.

  1. Determine an overall maintenance strategy on the equipment or system level on the basis of a
  2. cost/benefit analysis.
  3. Failure-mode-and-effect/RCM analysis.
  4. Gather preventive-maintenance activities and time intervals.
  5. Determine required sensors and implement a data-collection system for them.
  6. Identify gaps and overlaps in maintenance practices and schedules.
  7. Improve and refine procedures; recalculate inspection intervals for preventive maintenance.
  8. Implement system to follow up both condition-based and preventive-based maintenance.
  9. Establish procedure to adapt to changes in operational profiles or to system upgrades.

Big-Data Analytics

To enable a move toward predictive and proactive maintenance, a holistic approach is needed for the condition-monitoring systems used. Because of technology advances in recent years, analytics methods supporting improved maintenance planning can now be performed on the component, system, plant, or fleet level. 

As access to data improves, new methods of analytics can be applied to support fault detection, diagnosis, or prediction. The use of data fusion and machine-learning technology can enable accurate pinpointing of root causes of problems in highly interconnected systems.

While analytics on the component, system, or plant level is primarily used for fault detection and diagnosis, fleet analytics brings additional possibilities because it enables estimation of the likelihood or frequency of events. When data are available from a range of identical equipment operating under similar conditions, it will be possible to build an accurate model of the degradation process of the equipment. This type of analysis can enable accurate prediction of when a fault will occur. 

One of the major challenges in introducing condition-monitoring solutions is that it is very difficult to prove the real return on investment of a new maintenance regime. As more data from equipment become available on a fleet level, it will enable comparison of the performance of equipment under a traditional maintenance regime with equipment maintained with modern, condition-based methods. Allowing access to data on a fleet level can help the industry build momentum for a shift toward predictive and proactive maintenance.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 27788, “Digitalization of Oil and Gas Facilities Reduces Cost and Improves Maintenance Operations,” by H. Devold, T. Graven, and S.O. Halvorsrød, ABB, prepared for the 2017 Offshore Technology Conference, Houston, 1–4 May. The paper has not been peer reviewed. Copyright 2017 Offshore Technology Conference. Reproduced by permission.

Digitalization of Oil and Gas Facilities Reduces Cost and Improves Maintenance Operations

01 December 2017

Volume: 69 | Issue: 12

STAY CONNECTED

Don't miss the latest content delivered to your email box weekly. Sign up for the JPT newsletter.