Large mature fields have complex production facilities. Even facilities with the most elegant design of their day, with the best planned and executed maintenance programs, eventually
Figure 1. The purpose of an integrated asset model is to model reservoir and facilities in a collaborative manner, positioning each model in a sequence so that the output of one model is the input to next. (Images courtesy of Schlumberger)  
require band-aid fixes and repairs, ultimately leading to complex and often unpredictable production environments. Not even the most experienced engineer or field operations personnel can know for sure what the field-wide impact will be for adjustments on a single well, making the results of additional production optimization activities uncertain.

The emergence of integrated asset models is helping to address this problem. This new modeling approach lets operators model and simulate the most complex of production facilities at an asset level. These models allow asset teams to simulate and study the asset-wide impact of production optimization strategies before they are implemented, helping teams make better decisions more efficiently.

In 2006, Pemex E&P, the national oil company of Mexico, was facing problems common to a 25-year-old producing facility. These problems included facility sizing and constraint issues, including pipeline slugging and asset bottlenecks. Because of the complexity and size of the asset, Schlumberger and Pemex decided to use an integrated asset model to analyze the entire asset and propose viable solutions to these problems.

Using this approach, Pemex was able to identify and implement changes that have reduced operating costs by US $600,000 per year and increased production by 1,500 bbl/d and 5 MMcf/d for a combined estimated value of more than $35 million per year. Additional increases in production are anticipated upon implementation of all recommendations.

Integrated asset model solution

An integrated asset model is a representation of three main domains: 1) reservoirs, 2) wells and surface facilities, and 3) processing plants (Figure 1). These main domains are connected and integrated with other disciplines, including production surveillance, petroleum engineering, artificial lift optimization, facility design, economic analysis and planning tools.

An integrated asset model positions each of the three domain models in a sequence so that the output of one model is the input to next, so that the entire operation is modeled in a collaborative manner. The integrated environment includes the operating constraints of the individual models, but they are set to solve as a single comprehensive system. This linkage and system-wide constraint simulates the complex interaction of the wells, networks and facilities in an asset.

This comprehensive system approach allows asset teams to perform analysis and what-if scenarios and view results across an entire asset.

Ultimately, it is intended that the integrated asset model also be integrated to real-time data systems so that the model can be continuously, automatically updated with production data, keeping the model current and most useful to the asset team for accurate analysis and results.

Project background
San Manuel-Muspac, located onshore in southern Mexico, was producing more than 278
  Figure 2. Avocet Integrated Asset Modeler allowed the existing operations and facilities at San Manuel Muspac to be modeled, evaluated and revamped to reduce bottlenecks, increase production, eliminate pigging runs and reduce operating costs by $600,000 (USD) annually.
MMcf/d and 13,100 bbl/d oil from 69 wells through six processing facilities when the integrated asset model project was first considered. A 25-year-old production complex, it was initially designed for much higher production rates but was now experiencing production bottlenecks and some counter-pressure problems. The complex was also experiencing condensate slugging in the transport lines, due in part to irregular topography and lower gas velocity rates. To address this condensate problem, the operator was running up to 42 pigging operations per year in the gas lines.

Because of the complexity and size of the asset, the service company and operator decided to use an integrated asset model to analyze the entire asset. The goal for the project was to model the entire asset from wells and gathering networks to pipelines and processing facilities and then analyze the hydrocarbon transportation and processes in the surface facility and develop economically viable alternatives to improve the facility’s utilization and production. The analysis included examining the known problem of gas condensate hold-up in the main gas transmission pipelines.

How it works
Simulation models for the well’s flow dynamics were constructed using PIPESIM production analysis software, which included 69 wells and 13 network models. The operational behavior of each of the six facilities was modeled using Aspen HYSYS process modeling software from Aspen Technology Inc.

The Schlumberger Avocet Integrated Asset Modeler was used to link these individual models and to model the asset from the wells to the delivery point to create and an asset-wide simulation model in the facility’s current operating state (Figure 2).

Using the integrated asset modeler software, the team analyzed alternatives to the current production system. Any change in one part of the model initiates a full system resolve, determining the impact on the entire asset. An iterative bi-directional pressure/rate (P/Q) solver in the software ensures mass and energy balance for the entire integrated asset. The integrated model also allowed the team to quantify production gains, facility power requirements and flow assurance indicators for any and all points in the system.

For large complex brownfield projects, a phased implementation plan is crucial. The phases for this project included:
Phase 1: Data gathering and validation. Gather, validate and organize the data and information needed to build the individual simulation models and understand the facility’s current operation.
Phase 2: Training. Instruction for the individual modeling software and for the integrated asset modeler software. Use of an integrated asset model also changes workflows because the new model itself provides a common model around which the entire team can collaborate. Workflow changes can be discussed in training.

Phase 3: Well, network and process facilities modeling. Create and adjust the individual simulation models representing actual facilities behavior. Identify the main bottlenecks in the system. Integrate the individual models in the integrated asset modeler environment.
Phase 4: Integrated system evaluation. Evaluate alternatives for de-bottlenecking and optimization using well, network and process models in the integrated environment. Document and rank technical and operational feasible alternatives based on their production increase, cost reduction and implementation cost.

Phase 5: Economic analysis of chosen optimization alternatives. Evaluate alternatives that involve additional investment for the operator.

A crucial factor for project success was the ability to match the real operational conditions of the field to the simulation models used by integrating field data. This data matching was the basis for simulation model validation and detecting production optimization opportunities in the system and generating the recommendations that resulted in the production increases and operational cost reductions.

The field data included daily measurements and testing, all of which were consolidated in a single database that was updated daily. Measurement data included operational data for wells, streams and facility samples, which were recorded daily to determine physical and chemical properties of each phase, liquid and gas, and testing to monitor production behavior at all of these levels.


Asset evaluation indicated several opportunities to improve surface facilities utilization, reduce operational risk factors (by lowering pressure in the system), increase production, and enhance gas and condensate handling capabilities.

Recommendations were made accordingly, including implementing a cooling and separation system at a gas pipeline inlet. The addition of this system is expected to improve gas and condensate handling, further reducing operational problems associated with deferred production.

Other easy-to-apply recommendations were executed to increase production, such as bringing inactive pipeline legs back online and bypassing separation facilities. The implementations resulted in the reduction of the system back-pressure and the decrease of operational problems associated with liquid slugs in gas transfer lines.

Additionally, recommendations for wells (such as converting some wells from tubing flow to annular flow and modifying gas lift rates) are estimated to result in a total production increase of 6,900 b/d and 5.8 MMcf/d. While these implementations are still underway, production to date has increased by 1,500 bbl/d and 5 MMcf/d by implementing one of the four recommendations made.

Along with increased production, the integrated asset modeler software was used to identify cost reduction opportunities that have netted $600,000 per year in operational savings. Simulation showed that re-looping existing pipelines combined with other de-bottlenecking recommendations could reduce the frequency of pigging runs, which had been used to eliminate condensate hold-up in gas pipelines. To date, pigging runs in one pipeline have been eliminated, down from 22 in the previous year. In another pipeline, pigging runs have been reduced by 55% for a combined 90% reduction.


Based on these results, economics include a monthly revenue increase of $3 million and operation cost savings of $600,000/year. These figures are derived using $50/bbl oil, $5.30 per Mcf and pig run cost of $15,000.