Well-Test Optimization and Automation

Cramer, Ron; Jakeman, Simon Victor John; Berendschot, Leo

doi:10.2118/99971-ms

Cited by 31 publications

(6 citation statements)

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“…They are now being introduced for reservoir and well applications. They can estimate reservoir properties for reservoir monitoring and reservoir simulation model updating (Naevdal et al, 2002, Naevdal et al, 2003, Wen and Chen, 2005; but also be applied to gas-lifted wells (Bloemen et al, 2006), underbalanced drilling (Lorentzen et al, 2001), conventional and multi-lateral wells , Kruif et al, 2008, gas cone allocation in multi-zone reservoirs (Gryzlov et al, 2009) and well-testing optimisation and automation (Cramer et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Malakooti¹,

Muradov²,

Davies³

et al. 2015

All Days

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The value added by intelligent wells (I-wells) derives from real-time, reservoir and production performance monitoring together with zonal, downhole flow control. Unfortunately, downhole sensors that directly measure the flow rates and phase cuts required for optimal control of the well's producing zones are not normally installed. Instead, Multi-zone, Multi-phase Flow Metering (MFM) parameters are calculated from indirect measurements (e.g. from zonal pressures, temperatures, and well flow rates).To-date all published techniques of zonal flow rate allocation in multi-zone I-wells are "passive" in that they calculate the MFM parameters for a fixed given configuration of the completion. These techniques are subject to model error, but also to errors stemming from measurement noise when there is insufficient data duplication for accurate parameter estimation. This paper describes an "active" monitoring technique which uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve (ICV) positions during a routine multi-rate test in a "intelligent" well. This novel approach maximises the accuracy of the calculated reservoir properties and MFM parameters.Four "active monitoring" levels are available. Each one uses a particular combination of down-hole and surface measurements depending on the available well performance monitoring systems. Level one is the simplest, requiring a minimal amount of well data. The higher levels require more data; but provide, in return, a greater understanding of the in-flow performance, the volumes of produced fluids and the reservoir's properties at both a well and a zonal level.Our "active monitoring" workflow can be extended to a wide range of production and injection allocation problems for any well completion type. The workflow may also be used to improve the accuracy of production flow rate measurements and, ultimately, to maximise oil production rate and recovery.

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Section: Introductionmentioning

confidence: 99%

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Malakooti¹,

Muradov²,

Davies³

et al. 2015

All Days

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“…[23]), metered flows throughout an entire oil and gas production network are now often available in real time in the office domain and are archived in databases. In some cases, even individual well production is either measured or estimated in real time (references [10], [11], [12]). The availability of real time data allows the concepts of conventional daily or monthly network flow surveillance and reconciliation to be extended to real time operations.…”

Section: Reconciliation and Cross Validation Over A Large Networkmentioning

confidence: 99%

“…In general, wells have uncertain multiphase production, and the level of production is conventionally measured using test separators and multiphase meters, nominally once a month (references [9], [12]). In general, wells have uncertain multiphase production, and the level of production is conventionally measured using test separators and multiphase meters, nominally once a month (references [9], [12]).…”

Section: Challenges and Solutionsmentioning

confidence: 99%

“…The above has been achieved using the Shell developed commercially available FieldWare TM Production Universe TM (FW PU) software package (references [11], [12], [13]). Combined with the Metering Atlas production flow measurement information portal reported in [14], the work reported here also provides an integrated view of the status of production metering in an Asset.…”

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confidence: 99%

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Asset-Wide Reconciled Production Monitoring—A Key Enabler to Successful Real-Time Field Management

et al. 2010

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This paper describes how Brunei Shell Petroleum Co Sdn Bhd (BSP) uses cross validated and reconciled real time production data across large offshore and onshore production networks to support more proactive day-to-day oil and gas production surveillance and management. Examples are presented of the gathering network surveillance systems for the extensive BSP East and Darat Production Assets.In modern oil fields, there is an abundance of instrumentation distributed over often large geographical areas. The production network ranges from the individual wellheads, to production manifolds, test and bulk separators, past surge vessels and export pumps, to crude dehydration tanks at a central crude terminal or to the gas compressors and gas export points. At least the initial parts of the production gathering network at the wells will have multiphase flow with uncertain and variable oil, water and gas proportions. Normally the most downstream flow meters are well calibrated with good calibration records, but the actual accuracy of the majority of the flow meters upstream will be uncertain, and they will not be nominally designed to handle multiphase fluids or mixtures of fluids with varying densities. Conventionally, it is regarded to be problematic to track production variations in real time across the network back to the source wells. This is due to issues with metering multiphase flows and assumed uncertain transport delays.It is shown here that it is now practical to track in real time production rates across large production networks, working back from the most downstream, well metered, points to the individual bulk separator units or production platforms or the wells themselves. Indeed, it is possible to obtain a consistent, real-time reconciled view of the current production rates across these larger production networks. This allows more accurate surveillance of production from the wells, early detection of measurement issues and quicker responses to system upsets. Various common blockers to real time network wide production flow surveillance are addressed, including noisy meters, storage tanks, on-off pumps and hard-to-measure multiphase flows from the wells. The work reported may be seen as a "top-down" approach that complements the existing metering systems, integrating the data to "make the best of" all available metering and instrumentation. An example shows how better metering and instrumentation, virtual metering and real time data analysis are combined to detect and correct significant discrepancies.Raw ultrasonic flow meter signal in black, filtered signal is in red, reconciled filtered signal is in blue.

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“…Cramer et al [2] proposed a tool for optimization and automation of well tests. A well test schedule is provided by the user, and the tool is able to retrieve the required measurements.…”

Section: Introductionmentioning

confidence: 99%

Optimal Well-Testing Strategy for Production Optimization: A Monte Carlo Simulation Approach

Bieker¹,

Slupphaug²,

Johansen³

2006

SPE Eastern Regional Meeting

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TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractWell testing may be performed to support many decisions including ones related to production optimization of an oil production system. In production optimization information such as gas oil ratio and water cut/water oil ratio is used to decide on, for example, which wells to prioritize for choking back/opening to avoid over-/underutilization of the production capacity. Since the reservoir properties change with time, the uncertainties of their estimated values increase with time, and eventually a new well test will be required. As the uncertainty in the estimates grows, so does the risk for prioritizing the wrong well(s) giving lower oil production rate than possible. A computer program is developed to decide which well to test based on historical test data. The program uses a Monte Carlo approach for identifying which well test is likely to lead to highest increase in the total oil production rate after the well test information is utilized to optimize the oil production. The computer program is applied to field data quantifying the benefits when applied to this specific field.

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Well-Test Optimization and Automation

Cited by 31 publications

References 0 publications

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Asset-Wide Reconciled Production Monitoring—A Key Enabler to Successful Real-Time Field Management

Optimal Well-Testing Strategy for Production Optimization: A Monte Carlo Simulation Approach

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