With real-time data being captured from producing wells at record rates, operators are challenged to put this exploding volume of data to use. It is so voluminous it is nearly impossible for operators to develop understanding of the process in ways that enable effective action. This requires an analytical methodology that converts the volumes of data into actionable information.

The PEMEX integrated asset team lowered costs and improved production. (Image courtesy of Schlumberger)

Take the theoretical case of an onshore field with 100 producing wells. Each well produces gas with associated liquids, including some water. Production is gathered in 15 miles (24 km) of pipeline, including three compressor stations.

Temperature, pressure, and flow data could be available from each well — 300 data points read every 10 minutes. In perhaps 50% of the wells, there are electric submersible pumps or plunger systems that may give another five to 10 sensor readings.

At this rate, it becomes impossible for a person to digest the information and understand the implications of the changing data points on the twin objectives of more throughput and lower cost.

Challenges present opportunity

An integrated solution provides reservoir to sales point modeling.

This deluge of data creates a tremendous opportunity to leverage engineering modeling to get actionable information out of the vast sums of data. Models that were essential for designing what to build can be used with the real-time data to advise the operator what the system should produce under the conditions measured and what actions should be taken to get performance that maximizes the design’s capabilities.

Process models also can analyze system performance over time as conditions change (e.g., the composition of the fluids in the pipeline, pressure from wells, amount of water cut, etc.). These models can be tuned to optimize throughput, cost, or power consumption, etc., and help the operator understand and achieve its twin objectives.

Refining and chemical plants have faced this complex optimization problem for years. Using hydraulic, compositional, and flowing pressures combined with models of individual units (i.e., crude distillation, Hydrocracker, Hydrotreater, etc.) they have managed to contain cost and optimize throughput, which many have dubbed “operational excellence” initiatives. These businesses driven by razor-thin margins have proven the value of this approach over the years.

Business differences do not change the “operations excellence” challenge; they just make it more interesting.

The road ahead

Looking ahead, E&P operators are re-defining how they view operational excellence in ways that optimize production and costs simultaneously. As part of this process, leading E&P firms are moving in the direction of model-based decision support. The drivers are things that continue to evolve, such as a challenging economy and restricted access to drill for new reserves, in the case of national oil companies, the driver is the desire to find and develop their own reserves.

The net effect is a drive to get more from what has already been discovered. Pushing recovery rates from the range of 30% to 50% to a range of 60% to 80% can be a huge boost to supply while avoiding the risk associated with exploration.

There are three enabling technologies for accomplishing this. The first component is operating facilities as reliably and optimally as possible. The second is managing reservoir drainage through the wells as efficiently as possible. The final enabler is optimizing the first two components together. Surface facilities can be done today, but it is not widely adopted in E&P operations. These assets are like refineries and chemical plants, just with a bit more uncertainty, which requires the need for flexibility and sometimes quick reaction.

Optimization at work

The connection between wells and reservoirs is tougher because the time frames and scale are so different. Operating changes such as drawdown pressures on a well increases production, but it can impact reservoir recovery, and the effect may not be known for years. As a result, most of the successes today have focused on wells and surface facilities, though there is still much to do.

Some examples can clearly demonstrate how the surface facility and well optimizations have been brought together.

A production system for a project encompassing 10 fields, 64 wells, and six facilities that are part of PEMEX’s portfolio of assets was modeled with the objective of providing production advice to the operators.

In addition to the modeling technology, the workflow among several disciplines was key to the results achieved. PEMEX improved production (2,000 b/d for oil and 5 MMcf/d for gas) and lowered costs. Another advantage to this approach is the ability to plan longer term around various production scenarios by more accurately estimating impacts and potential facility changes.

The assets for another project were in the North Sea and involved combining Petroleum Experts’ capabilities with AspenTech’s process modeling and optimization technology to optimize production performance and identify bottlenecks and disconnects between the reservoir capacity and asset infrastructure.

The solution showed what thoughtful use of integrated modeling can achieve for the business:

• Increased production 8%;

• Enhanced engineers’ productivity;

• Identified bottlenecks and diagnosed problems; and

• Allowed for better communications among disciplines.

The results can be counterintuitive (requiring support to understand the system and gain confidence in the results of the integrated approach), but the benefits are dramatic.

BP recently discussed at the GPA and AspenTech user conference the benefits it achieved in Trinidad by modeling a pipeline network and gas processing plant. At the Society of Petroleum Engineers (SPE) annual event in New Orleans this year, the company elaborated on this success. The operator had reported more production, but in the SPE presentation, the company shared the improvement in ultimate recovery by lowering cost and pressure lost in the system.

The final example involves Production Systems Network’s (PSN’s) use of dynamic modeling to improve safety in operations. Working for a major operating company, PSN evaluated the interaction of compressor, emergency shutdown (ESD), and emergency de-pressuring (EDP) systems with the goal of improving operations safety. By using a dynamic and integrated modeling approach, the company was able to lower capital costs and retain safety margins. Plants are normally designed with steady-state simulators. However, these plants operate in the dynamic real world, where changes through time constitute one of the most important variables. The result was a US $3.3 million reduction in capital cost.

The company also was able to provide a tool to operators that helped them understand the interaction between ESP and ESD systems in a complex asset. The personnel who operate process safety systems frequently have a poor understanding of how they work and what to do if something goes wrong with them. Models can help them understand the entire process better.

Engineering models that support operations will take E&P firms to the next level in achieving operational excellence. E&P is a dynamic business. Models of the complex processes allow operators to produce more and at a lower cost. And safety is enhanced when everyone understands the process.