Assets operating with FieldWare Production University (PU) are seeing improved safety, increased production, reduced costs, reduced well testing and improved hydrocarbon accounting. The technology is well established and the major challenges now are to roll out to all assets and to sustainably transform the associated work processes. The purpose of this article is to summarize operator experiences to date.

Figure 1. The software offers operators multiple data sets as models. (All graphics courtesy of Shell Global Solutions)
PU is a software application that transforms real-time data from commodity instrumentation into estimates of oil, water and gas rates for all wells all of the time. Continuously knowing what the wells and reservoirs are producing facilitates improved asset management and integrity.

Introduction

A key upstream exploration and production (E&P) operations question is how effectively do we manage our wells and reservoirs? The trite answer is inadequately due to lack of a continuous, reliable measure of well/reservoir performance on more than 90% of wells! If we do not measure (continuously), how can we manage? How do we monitor well and reservoir performance? How do we perform hydrocarbon accounting? How do we report production from wells?

Traditionally, discontinuous well testing is used to determine well performance — wells are tested, say, once per month, and it is assumed that for the other 29 days the wells produce the same — Mother Nature is usually not that predictable. Also, the quality and accuracy of well tests often are unsatisfactory — tests are rejected and have to be repeated. Regulatory authorities specify a given well test minimal frequency, e.g., once per month per well. Continuous well testing would be much more desirable. However, it is impractical to install a test separator for every well. At the heart of this conundrum is multiphase flow. Very few wells deliver a clean, measurable, single-phase stream, and it is impractical to install multiphase flow meters on all wells.

PU is a software application that continuously estimates oil, gas and water flows for all of the wells all of the time. The software enables improved well surveillance, more accurate hydrocarbon accounting, automatic production reporting and production optimization. It safeguards the technical integrity of wells and reservoirs — for example, early detection and control of gas, or water breakout. It is cost-effective in that it requires minimal, commodity instrumentation and information technology systems, much of which may already be present in field operations. Successful deployment requires changing the way we operate and motivating the people involved — this can be much harder than deploying the technology.

Dynamic data

PU uses dynamic data-driven models of the production system. The well models estimate water, oil and gas production flows in real time, primarily from existing well instrumentation.
Figure 2. Information fed to the separator provides updated information about individual wells tied to that separator.
Effects such as back-out of weaker producers at headers are captured in these models. Physical models are not used — no well tubing diameters, no roughness, no fluid properties, no near wellbore “skins” and no pre-assumed multiphase flow correlations. Real-time well measurements are related to volumetric flows from test separators.

The data-driven approach has been proven to be robust and usable in the oil and gas production environment.

A key aspect of PU is the Deliberately Disturbed Well Test (DDWT), which is used to characterize well performance. These tests go beyond traditional production well testing. The objective is to relate well production (oil, gas, water) to measured well parameters (e.g., flowing tubing head pressure, downhole pressure, gas lift injection rate, temperature). The emphasis is on capturing the response of the well to step changes in controllable parameters.

The upper screen of Figure 1 shows DDWT data in which five data sets are used to model the well. In the lower screen the parameters used in the respective models can be viewed. Models can be selected/deselected in the upper right hand panel to achieve a best model fit.
Once created, the individual well models are used to compute the well production per stream. The software accumulates daily flow per well, which reflects the actual producing conditions, including trips and restarts and plant operating mode changes.

A simplified abstract topography is constructed relating wells to a calibration point. Typically the calibration point is a bulk separator, preferably providing oil, water and gas measurement on a continuous basis.

PU production data per well are compared and reconciled automatically against the installation’s overall export meter. This provides a reconciliation factor for each produced/injected stream on a continuous basis for the current day and the last 24 hours. There is also a diagnostics panel alerting the user of production systems events such as event detection, single point measurements or complex logic to detect specific events e.g., contamination of the water disposal stream with oil. There is also an information panel that alerts the operator about defective instruments and communications infrastructure.
With this single screen an asset manager can gauge the current health of his production systems. If all the reconciliation factors are within acceptable bounds, he knows his production system is under control — well models accurate, instruments working, communications highway functioning. If this is not the case, it is possible to drill down to process, header and well level.

The output from the measurements on the bulk separator provide a continuous 24-hour-a-day, 7-day-a-week data stream. PU uses the dynamic variation seen at the calibration point to further tune its well models. Plant trips and restarts are very visible and generate useful data, especially when the field is brought back on line. The dynamic well models are updated every 24 hours to reflect the total information available in the preceding period, allowing tracking of decline in well rate, increase in GOR and water cut.

PU thrives on dynamics (e.g., well bean up/bean down) to continuously update individual well models. Normal E&P operations provide a dynamic environment with well interventions, process trips, etc. If assets exhibit stable production with minimal dynamics, then dynamics can be introduced. Wells can be beaned up/down for short periods to cause transients to ripple through the process. Single or multiple disturbances can be introduced simultaneously. These pseudo tests are known as Deliberately Disturbed Production Tests (DDPTs). If these tests are insufficient to realign the models, then PU initiates a full DDWT.

Contrast PU’s low maintenance, data-driven, production workflow-based well test modeling approach with harder-to-sustain, specialized physical models requiring frequent tuning by process engineers. Building and upkeep of physical models is hard is hard to sustain. PU models are based on well testing, which is very much part of the oil and gas operational workflow. Also, PU is more tolerant to errors in process measurements and instrument drift over time.

Operator experience

PU implementation has yielded a wide-ranging list of “bottom-line” benefits from Shell operations.
The major areas of benefit seen to date are:
Reduction in production decline rate. In moving to real-time operation and optimization of a number of fields, a significant reduction in well production decline rate has been noted.
In one particular field a significant reduction in annual decline rate has been achieved and sustained. The reduction is in part attributed to more stable operation of the field and constant attention to well performance and adjustment of well parameters, e.g., early detection of coning well, water break-through and manual optimization.
Optimization improvements. By having a good understanding of well performance and the effect of changing separation pressures and well routing, changes in performance are immediately visible. Where changes have a negative impact they can be reversed immediately. Historically, the cycle time for optimization was days if not weeks. With the current version of PU, optimization of a field is possible on a daily or hourly basis from the office desktop.
Reduction in deferred production. With PU data available in real time, underperforming wells can be detected quickly and compensated by adjusting other wells or opening up closed in wells. The net result is that for fields where PU has been implemented a significant reduction in deferred production has been achieved.
Focused use of resources. Implementation of PU has reduced the time spent by production technologists and operations engineers on gathering and validating well data and making decisions. This has allowed the redeployment of scarce staff to other tasks. In one operation a single operations engineer runs a number of fields where previously five staff members were involved.
Production forecasting improvements. With better well performance data it is possible to provide more accurate short- and medium-term forecasts. This has been evident in one particular field where actual production is running consistently 5% above forecast. Previously the gap between forecast and actual was consistently on the negative side. The improved allocation of production to wells will profoundly influence the accuracy of long-term forecasts delivered by reservoir simulators.
Stabilization of production rates. On a number of fields there has been a marked reduction in the variance in daily production. The peaks and troughs have leveled off. This again is thought to be a result of the attention paid to individual wells, daily field review and optimization, and a reduction in plant trips.
Reduction in well testing. In one asset no routine well tests were conducted for a period of 15 months. PU continued to run and track well performance and adjust the well models. Reduction in well testing duration has been seen in other fields. With PU in place, a larger number of wells can be allocated to a test facility. This has an impact on cost where it may be possible to operate with a single test train as opposed to two trains. Also, the test separator can be released to operate as a bulk vessel or for well service activity for longer periods. In fact, PU lets you know when a well needs to have a well test.
Reduced HSE exposure and logistics cost. In another Shell operation the introduction of PU has allowed the release of one service/transport vessel with significant reduction in annual costs. The need to have operators regularly visit a remote offshore location was removed. The reduction in associated HSE risk is a major benefit.
Production surveillance instrumentation and measurement data improvements. PU has become a catalyst, focusing attention on the quality of production surveillance instrumentation and measurement data. As PU is visible and used actively from the “coal face” to asset manager level, it becomes very apparent when instrumentation or measurements drop out of service, manifested by well model predications deviating significantly from export flows.

Conclusions

A PU microcosm is well established in multiple assets, sufficient to establish significant benefits for 20 projects completed to date:
• Significantly increased production due to improved surveillance and optimization;
• Significantly reduced operating costs due to optimization e.g. reduced gas lift gas and logistics savings due to reduced travel to the wells; and
• Safer operations due to reduced operator exposure to hazard.
The challenge now is to scale up these benefits to full global brown/green fields operations and transform the traditional manual operations culture into a new “Smart Fields” way of working based upon remote surveillance and control.

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