Theory of constraints and other production philosophies, including lean, will increasingly be applied in upstream oil and gas (Courtesy Carol A. Ptak and Eli Schragenheim; ERP: Tools, Techniques, and Applications for Integrating the Supply Chain)

Oil and gas producers have implemented a wide variety of accounting, performance measurement, and collaboration automation techniques. However, in most instances, production performance measurement and supply-chain accounting measurement have not merged successfully. One reason for this is because accounting and performance data has rarely been actionable. The source data was either unavailable in a timely fashion or was presented in “patterns” that did not help users make better decisions. As a result, effective problem intervention or exploitation of supply chain opportunities is thwarted.

Most of these applications amount to a kind of “browsing” based on reactive rather than proactive intervention. Surveys show operators and technical personnel who contribute to the facility performance spend up to 60% of their time finding and processing new information, often in response to an unplanned event or unexpected supply chain opportunity.

These trends contribute to the difficulties of increasing operations effectiveness. One way to mitigate the challenges is through use of a new generation of operations management systems that help employees work smarter.

Operations management systems (OMS), such as Invensys Process Systems’ “InFusion”-based industry solutions can increase throughput and recovery by more than 5%, as well as reduce production costs by as much as 15% and defer capital spending by up to 30% each year. InFusion is very much in the mainstream of what’s new in information technology in that it involves aggregation of diverse information sources, served up as a collaboration engine.

Since its appearance in the workplace, software’s role has been to take a serial process — work passed from one set of hands to the next — and make it concurrent. The Internet and advances such as portals, composite applications, and service-oriented architecture make it possible to overcome integration barriers. An OMS gives operators, technicians, engineers, and managers a shared view of production, maintenance, and business performance. OMSs are enablers for the digital oil field, helping companies refine, understand, and improve their own practices. But what must also be understood are the changed methods for sound decision-making that follow from the new technology infrastructure.

What does working smarter mean?

Operations teams in oil and gas production must make safer, earlier, and better decisions to affect current performance of the facility and supply chain. These decisions often balance naturally conflicting demands amongst immediate facility and supply chain needs and long-term benefits. Saving the day by pushing production systems beyond their limit can often mean sacrificing longer-term goals such as extending well life, reservoir life, and periods between major overhauls.

One compelling approach to making better decisions comes from Dr. Carol Ptak of Pacific Lutheran University, a leading thinker on managing supply chains and balancing conflicting goals. She describes a conflict that pits optimization of the industrial facility against optimization of the supply chains that feed these facilities (Figure 1).

Waste in the oil and gas production is found in many activities: utilization, availability, efficiency, emissions, inventory, maintenance, penalties, and quality, especially for gas and condensate production. Tactics A and B apply Lean manufacturing, while tactics C and D apply the theory of constraints. Both Lean manufacturing and theory of constraints should be used, depending on the situation, but determining which tactic is most appropriate in a given situation requires a systematic approach.

Making more effective decisions includes acting early enough to prevent problems or exploit opportunities, and making better choices among conflicting goals. Many decision models provide guidance, but arguably the best one for processing or production facilities is the Boyd Observe, Orient, Decide, Act (OODA) loop.

The basic principle is that teams need to make decisions faster than the environment changes. When they encounter new, potentially valuable observations, decision makers seldom have enough time to orient themselves within the proper context to make the best decision.

OMSs typically use online modeling software and visualization to help individuals intuit information in context. Visualizations include processing and production time frames, violation alerts, and personnel assignments. These capabilities can lead to behaviors as follows:
• Teams make consistently good, early, and well-informed decisions (including “do nothing”);
• Teams effectively intervene when undesired events occur, and exploit more desired events (supply chain opportunities);
• The organization achieves an appropriate balance of facility and supply chain targets;
• Specialists focus on continual improvement and new situations;
• Departments build trust — in themselves and other departments.

Smart operations can reduce uncertainty and reliably contribute to sustained growth in operations effectiveness.

Demand/capability change. Within the context of an OMS, everyone views forecasts of targets and capabilities in a common visual format, measured in volume/mass and money. As a result, performance that varies by shift, facility, and product can be easily compared; teams and departments make better decisions about current commitments and justifying improvements. The organization’s culture learns to trust itself and others in an environment where performance is visible. Managers can quickly recognize points of no return if longer production runs or performance intervals cannot achieve key targets. Forecasted trends in demand/capability shortfalls drive teams to make decisions that affect current deliveries and future targets and commitments. Schedulers, controllers, or dispatchers make informed decisions about allocating schedules across a fleet of production areas and sites. Technical personnel have earlier and better information to justify and plan debottlenecking or expansion projects.

Critical asset management. Decisions regarding critical assets — energy conversion, rotating equipment, instrumentation, separators, etc. — enable tradeoffs between current commitments and longer-term maintenance activities and production scheduling. For example, compressor performance analysis forecasts when efficiency would warrant changing the maintenance schedule. But this decision is balanced by information about opportunity costs and real-time risk assessment. This approach uses real-time calculations of supply chain impact, maintenance impact and operations risk impact. The same approach is used for well performance, motor efficiency, pump capacity, seal life, etc.

Smart operations with OMS

Achieving smart operations requires integration of many technologies, ranging from field devices to business decision-support software. Two key technologies used include online simulations and pattern recognizers. Online simulations try to understand everything based on first-principles math, but are weaker in handling dynamic events. These are better suited to trends in performance. Pattern recognizers, on the other hand, try to understand everything based on statistics. They are very useful for identifying future events, but are poorly suited for discerning trends and cannot estimate the timing of events. Workflows are more flexible and are used to make trade-off decisions. Work orders are more procedural, and often use workflows as a key decision element.

Effective, sustained improvement using the approaches outlined above requires a combination of performance measurement, teamwork, and trustworthy technology.