Subsea Multiphase Flow Meter Measurement Performance Assurance with an Applied Data Validation and Reconciliation Surveillance Methodology (OTC 2022)

Subsea Multiphase Flow Meter Measurement Performance Assurance with an Applied Data Validation and Reconciliation Surveillance Methodology (OTC 2022)

This paper addresses performance assurance of subsea multiphase flow meters (MPFMs) using a surveillance methodology based on Data Validation and Reconciliation. Subsea measurements are challenging to verify because direct reference measurements are rarely available. This creates uncertainty in production monitoring and allocation.

The proposed DVR-based methodology systematically integrates multiple data sources such as pressure, temperature, fluid composition, and well test data to continuously evaluate MPFM performance. The approach supports three main objectives: direct validation against well tests, continuous monitoring during normal operations, and quantitative estimation of measurement uncertainty.

Field applications illustrate how the methodology helps detect deviations, maintain confidence in subsea measurements, and support long-term production management strategies.

Paper on OnePetro

 

Authors

Emmelyn Graham, Libsen Castill, John Yi, Tom Mooney, Amin Amin, Timothy Jadot – OTC 2022 – Houston – 2-5 May 2022 – OTC-31836-MS
 

Abstract

Measurement performance assurance for subsea multiphase flow meters (MPFM) can derive motivation from several sources of technical and/or business need, ranging from well surveillance to flow assurance monitoring, to production allocation among commingled sources of varying royalty, taxation, or ownership. Often, the more sensitive the subsea MPFM measurement is to a technical or business driver the more difficult it can be to initiate a comparison to a reference measurement or reference fluids such as topside measurement. Thus, providing assurance for subsea MPFM measurement performance requires a coordinated effort of MPFM performance surveillance – a combination of data and activities that can enable continuous indication of MPFM measurement performance, with or without periodic comparisons with reference measurements. However, utilizing MPFM performance surveillance information – which can come from a multitude of sources – can be confusing and potentially misinformative if a rigorous methodology to systematize the information isn’t applied. It was in this context that a surveillance methodology using data validation and reconciliation (DVR) was chosen to leverage the disparate surveillance information available and provide quantitative measurement performance assurance results for a subsea MPFM.

DVR was applied to assess the performance of a subsea MPFM incorporated within a subsea/topside field. Multiple sources of surveillance data and information were utilized in the application including the subsea MPFM, independent water-liquid ratio measurement, pressures and temperatures throughout the network, fluid properties, inlet separator flow measurements, and well test results.

Three main objectives were established to demonstrate efficacy of the applied DVR methodology for subsea MPFM measurement performance assurance:

1) quantified DVR results for direct MPFM validation via well test;

2) continuous DVR condition-based monitoring (CBM) of the subsea MPFM within a defined subsea/topsides topology during normal operations, and

3) DVR-derived uncertainty estimates for the subsea MPFM.

Several case studies using DVR surveillance are presented to address subsea measurement performance assurance through direct validation, CBM and uncertainty estimation. Each case study describes the workflow and detailed explanations for the steps taken in the DVR surveillance methodology.

Implementation challenges and lessons learned are also presented, along with a strategy for sustained subsea MPFM measurement performance assurance using a DVR-based surveillance approach.

 

The Application of Data Validation and Reconciliation to Upstream Production Measurement Integration and Surveillance – Field Study (SPE ATCE 2021)

The Application of Data Validation and Reconciliation to Upstream Production Measurement Integration and Surveillance – Field Study (SPE ATCE 2021)

This paper presents the application of an advanced Data Validation and Reconciliation (DVR) methodology to a real offshore oil and gas field. The objective is to consistently integrate all available measurements, including well, surface, and fluid data, in order to automatically detect and correct measurement errors and reduce overall uncertainty in production data.

The DVR approach combines measurement redundancy with physical conservation laws to estimate the most probable production rates and to quantify their uncertainty. The methodology delivers coherent, real-time production data that can be used for production surveillance, well test validation, virtual metering, and production allocation. Field results demonstrate how DVR improves data reliability and supports better operational decision making.

Paper on OnePetro

 

Authors

Vincent van der Bent, Amin Amin, Timothy Jadot – SPE ATCE 2021 – Dubai – 21-23 September 2021 – SPE-205934-MS
 

Abstract

With the advent of increased measurements and instrumentation in oil and gas upstream production infrastructure; in the wellbore, in subsea and on surface processing facilities, data integration from all sources can be used more effectively in producing consistent and robust production profiles. The proposed data integration methodology aims at identifying the sources of measurement and process errors and removing them from the system. This ensures quasi error-free data when driving critical applications such as well rate determination from virtual and multiphase meters, and production allocation schemes, to name few. Confidence in the data is further enhanced by quantifying the uncertainty of each measured and unmeasured variable.

Advanced Data Validation and Reconciliation (DVR) methodology uses data redundancy to correct measurements. As more data is ingested in a modeling system the statistical aspect attached to each measurement becomes an important source of information to further improve its precision. DVR is an equation-based calculation process. It combines data redundancy and conservation laws to correct measurements and convert them into accurate and reliable information. The methodology is used in upstream oil & gas, refineries and gas plants, petrochemical plants as well as power plants including nuclear. DVR detects faulty sensors and identifies degradation of equipment performance. As such, it provides more robust inputs to operations, simulation, and automation processes.

The DVR methodology is presented using field data from a producing offshore field. The discussion details the design and implementation of a DVR system to integrate all available field data from the wellbore and surface facilities. The integrated data in this end-to-end evaluation includes reservoir productivity parameters, downhole and wellhead measurements, tuned vertical lift models, artificial lift devices, fluid sample analysis and thermodynamic models, and top facility process measurements. The automated DVR iterative runs solve all conservation equations simultaneously when determining the production flowrates “true values” and their uncertainties. The DVR field application is successfully used in real-time to ensure data consistency across a number of production tasks including the continual surveillance of the critical components of the production facility, the evaluation and validation of well tests using multiphase flow metering, the virtual flow metering of each well, the modeling of fluid phase behavior in the well and in the multistage separation facility, and performing the back allocation from sales meters to individual wells.

 

Modeling Production Facilities Using Conventional Process Simulators and Data Validation and Reconciliation Methodology (OTC 2021)

Modeling Production Facilities Using Conventional Process Simulators and Data Validation and Reconciliation Methodology (OTC 2021)

Conventional process simulators often ignore measurement uncertainty, which can propagate errors when inputs are biased or faulty. This paper proposes complementing simulators with Data Validation and Reconciliation (DVR), a statistical approach that uses all available measurements and their uncertainties to detect and correct errors, improve data quality, and quantify unmeasured variables. Case studies on surface facilities show that DVR leverages measurement redundancy to enhance surveillance, validate meters and samples, and support better design and operations decisions. The combined approach enables more robust monitoring, condition-based maintenance, and CAPEX/OPEX optimization through smarter instrument selection and soft-sensor substitution.

Paper on OnePetro

 

Authors

Tom Mooney, Kelda Bratley, Amin Amin, Timothy Jadot – OTC 2021 – Houston – 16-19 August 2021 – OTC-31082-MS
 

Abstract

The use of conventional process simulators is commonplace for system design and is growing in use for online monitoring and optimization applications. While these simulators are extremely useful, additional value can be extracted by combining simulator predictions with field inputs from measurement devices such as flowmeters, pressure and temperature sensors. The statistical nature of inputs (e.g., measurement uncertainty) are typically not considered in the forward calculations performed by the simulators and so may lead to erroneous results if the actual raw measurement is in error or biased.

A complementary modeling methodology is proposed to identify and correct measurement and process errors as an integral part of a robust simulation practice. The studied approach ensures best quality data for direct use in the process models and simulators for operations and process surveillance. From a design perspective, this approach also makes it possible to evaluate the impact of uncertainty of measured and unmeasured variables on CAPEX spend and optimize instrument / meter design.

In this work, an extended statistical approach to process simulation is examined using Data Validation and Reconciliation, (DVR). The DVR methodology is compared to conventional non-statistical, deterministic process simulators. A key difference is that DVR uses any measured variable (inlet, outlet, or in between measurements), including its uncertainty, in the modelled process as an input, where only inlet measurement values are used by traditional simulators to estimate the values of all other measured and unmeasured variables.

A walk through the DVR calculations and applications is done using several comparative case studies of a typical surface process facility. Examples are the simulation of commingled multistage oil and gas separation process, the validation of separators flowmeters and fluids samples, and the quantification of unmeasured variables along with their uncertainties. The studies demonstrate the added value from using redundancy from all available measurements in a process model based on the DVR method.

Single points and data streaming field cases highlight the dependency and complementing roles of traditional simulators, and data validation provided by the DVR methodology; it is shown how robust measurement management strategies can be developed based on DVR’s effective surveillance capabilities. Moreover, the cases demonstrate how DVR-based capex and opex improvements are derived from effective hardware selection using cost versus measurement precision trade-offs, soft measurements substitutes, and from condition-based maintenance strategies.

 

Evaluation of Flowloop Uncertainty with Live Hydrocarbon Process Fluids for Multiphase Flowmeters Validation (OTC 2017)

Evaluation of Flowloop Uncertainty with Live Hydrocarbon Process Fluids for Multiphase Flowmeters Validation (OTC 2017)

This paper shows a data-reconciliation-based process model with integrated thermodynamics used to validate multiphase flowmeters (MPFMs) in high-pressure flowloops with live hydrocarbons. By reconciling measured and modeled fluid properties, the method produces consistent reference flowrates with quantified uncertainties, accounting for redundancy and physical constraints. Applied to generic HP test points and a real high-pressure, high-volume multiphase loop, the approach shows how input uncertainties affect MPFM evaluation and allows operators to replicate realistic subsea conditions in a controlled loop without oversimplified assumptions.

Paper on OnePetro

 

Authors

A. Amin, E. Harsono, E. Kamies, G. Heyen, G. De Vos – Offshore Technology Conference – Houston – 1-4 May 2017 – OTC-27925-MS
 

Abstract

The validation of multiphase flowmeters (MPFMs) in a controlled flowloop using live hydrocarbon fluid is examined with emphasis on the impact of measured and modelled fluid properties uncertainty on the loop reference measurements.

A customized process model based on data reconciliation with integrated thermodynamics package is built to evaluate the loop reference flowrates and their uncertainties at the multiphase flowmeter test station conditions. The process model accounts for data redundancy and physical constraints to ensure consistency of the reconciled measured and unmeasured variables used in the validation of multiphase flowmeters.

The model is applied to a generic set of test points performed in a high pressure (HP) flowloop. The paper discusses the modeling and operational aspects involved in validating the subsea meter’s measured flowrates. It also highlights the sensitivity of different inputs and their uncertainties on MPFM performance evaluation.

For the first time, the proposed approach enables operators to achieve the reality of subsea operations inside a highly controlled flowloop environment without resorting to overly simplified assumptions and practices. The approach is further validated by applying it to a high-pressure, high-volume, true multiphase flowloop using actual hydrocarbon fluids.

 

Application of Data Validation and Reconciliation to Production Allocation (NSFMW 2016)

Application of Data Validation and Reconciliation to Production Allocation (NSFMW 2016)

This paper proposes and tests a practical way to use Data Validation and Reconciliation (DVR) to improve production allocation in commingled oil and gas systems by detecting bad measurements and reconciling data before allocation. It contrasts the traditional PSM + proportional (pro-rata) allocation approach, which balances only at export and does not integrate measurement quality, with a PSM-DVR workflow that uses process constraints, redundancy, and uncertainty weighting to “vote out” erroneous data and deliver allocations that remain physically consistent.

Using a simulated multi-tier facility (multiple inlet separators, two trains, intermediate nodes, export meter), the authors run several scenarios: moderate imbalance, adding export composition measurements, introducing a gross flowmeter bias, adding a sample composition error, replacing export composition with only density, calibrating meters and correcting sample errors, and finally losing an inlet flowmeter. Across cases, DVR provides diagnostics (global and individual penalties) that flag biased meters or bad samples, improves allocation robustness versus proportional allocation when gross errors exist, and can generate soft-sensor estimates for missing measurements when redundancy is sufficient. A key finding is that adding high-quality measurements (especially export composition) increases redundancy, improves reconciled estimates and uncertainties, and strengthens error localization, while reduced information (density only) weakens root-cause detection. The paper concludes that DVR is valuable at minimum as a surveillance layer to guide corrective actions before any allocation method, and that fully integrated PSM-DVR is preferred when DVR-based allocation is required to avoid model/EOS inconsistencies across separate software.

Paper on NFOGM

 

Authors

Robert A. Webb, Amin Amin, Boris Kalitventzeff – 34th International North Sea Flow Measurement Workshop – 25-28 October 2016 


 

Abstract

Generally, upstream oil and gas export measurements are made on separated and depressurized bulk oil and gas flow streams collected from a group of wells. In depressurized conditions, where phase separation can be ensured, single phase measurements can be made with the best possible accuracy. These measurements follow measurement standards and recommended practices, such as from the API MPMS, Chapter 20, Section 1.

Wherever export production contains fluids from more than one producer (or unique ownership group, whether it is for one well or several), there must be an equitable distribution of the production export to each and every contributing producer. The allocation process serves to determine in the fairest manner the quantities of oil and gas produced, flared, consumed for fuel, or otherwise spent out of the total export over a given time period for each contributing producer. The allocation process starts at the end of upstream production, from the point of custody transfer to the midstream transporter, and works back upstream to the source of production, the well. In determining each producer’s fair share of the export production, the resulting revenues and costs, such as production handling service fees, royalties and other costs, can be completely resolved.

The allocation process of quantifying the volume or mass of fluids produced from each well applies similarly to non-fiscal activities, such as the management of well performance, process facility operations, and reservoir recovery. While these applications are also important, they generally do not involve the resolution of inter- company financial transactions in accordance with an agreement. The elements of upstream metering and allocation carried out for fiscal allocation often encompass most of the needs for well or fluid allocation; however, the unique measurement requirements of reservoir and production management should be considered separately in order to get a complete set of metering and allocation requirements for all end uses of the flow measurement data.

Finally, if the allocation fully serves its purpose, it should be auditable and defensible. A good allocation minimizes disputes between partners in a production agreement. In practice, how is this achieved? Unmixing the mixed streams of hydrocarbons from different wells, zones and fields is not straightforward. It can be downright challenging, and it certainly can be done in different ways leading to different outcomes, which leads back to the possibility of dispute. A good allocation, therefore, is one that is agreeable to all parties involved.

For each producer to get a consistently good, equitable allocation, it requires:

  1. a written agreement that defines the objectives and methods of the allocation
  2. a metering system that can deliver the required flow and other measurements
  3. an auditable, independent execution of the allocation process

As with many physical phenomena, a deterministic approach to defining the outcome of certain pre-established procedures and agreements is not always realistic. Errors and uncertainties in measurements, processes, and models, visible and hidden, are also critical to the allocation process requiring attention and understanding by all parties involved, the producers and regulator alike. Identifying and understanding the sources of measurements errors is a necessary step to minimize their impact on allocation.

The risk of revenue loss by any of the parties, big or small, due to ill-defined statistical factors will strongly reflect on the sense of fairness felt by everyone. Such situation can trigger with individuals a sense of unfairness as they learn that the production allocated to their lease is deemed less certain than the one from next door despite the fact that they both use the same equipment to measure production! Can this knowledge be used to mitigate “unfairness” and reduce the exposure faced by producers and regulators in the execution of their duties?

Through the study of simulated production scenarios the paper highlights ways to detect and deal with errant data in production allocation data sets. It also proposes and evaluates a practical procedure that turns DVR error-qualifiable production data into allocated quantities the same way traditional PSM systems are used in production allocation. The difference is that in the latter approach the data qualification for potential data bias or imprecision is not integrated in the allocation process leaving room for production misallocation risks.

The evaluated approach is based on maximizing the use of all available process information (devices and fluids) in an attempt to “vote out” erroneous measurements once identified. Results are evaluated with and without the erroneous data in moderate cases where measurements cannot be replaced for cost or operational reasons. The Data Validation and Reconciliation (DVR), as the name implies, is evaluated for measurement error identification (surveillance functionality), and for its ability to make production estimates that qualify as allocated quantities of a relatively complex multi- tiered commingled production system. The current work builds on earlier effort that aimed at studying the use of measurement uncertainty in production allocation; Pro-rata, Uncertainty-based, and DVR-based methodologies [1].

DVR background and basic theory are first reviewed then followed by a review of the steps employed to perform traditional proportional allocation of volumetric quantities using Process Simulation Modeling (PSM). A PSM-DVR approach to allocation is proposed in the paper and tested by comparing the results to the “True Values” of a Reference simulation and to the results obtained from the PSM-Proportional Allocation methodology. However before performing allocation calculations, various production scenarios are simulated with various types of measurement and fluids property errors to test DVR’s surveillance capabilities. Proportional and DVR based allocation is carried out with and without errors and with varying amount of information to examine the robustness of allocation answers of each allocation methodology. The results are compared with the process “True Values”.

The paper continues in summarizing practical considerations and recommendations on the use of DVR in surveillance and/or allocation applications. It is also shown that if DVR-based allocation is not adopted for commercial or contractual reasons, other allocation methodologies will continue to benefit from a parallel DVR implementation for surveillance applications. Moreover, while the determination of measurements uncertainties has direct relevance to monetary arrangements if DVR is used for allocation, the constraints in quantifying the uncertainty values can be relaxed if the DVR process is used for surveillance only.

Using Measurement Uncertainty in Production Allocation (UPM 2016)

Using Measurement Uncertainty in Production Allocation (UPM 2016)

This paper shows how production measurement uncertainties can significantly affect allocation results under proportional and by-difference methods. This paper evaluates these impacts and proposes an uncertainty-based allocation using data validation and reconciliation (DVR). DVR incorporates measurement and fluid-property uncertainties, balances the full production system from subsea to custody transfer under physical constraints, and estimates the most likely sales quantities. Applied for the first time to a multi-tier allocation system, the method quantifies both measured and unmeasured variables with their uncertainties and provides diagnostics that support condition-based measurement maintenance, leading to fairer and more defensible allocations.

Download paper

 

Authors

Amin Amin – Upstream Production Measurement (UPM) Forum – Houston TX, – 24-25 February 2016.

 

Abstract

The effect of production measurements uncertainties is evaluated on different oil and gas production allocation methods, proportional, by difference, and uncertainty based. The scope is further expanded by examining and proposing an allocation method where measured and allocated quantities uncertainties are considered to achieve fair and equitable production allocation results.

The proposed approach uses data validation and reconciliation (DVR) methodology (governed by establish industry standards) to account for the measurement and fluids properties uncertainties rarely considered in traditional production allocation schemes. The DVR approach has the advantage to balance a complete production system, from subsea to custody transfer point, by estimating the most likely flow quantities available to sales within the known accuracies of the measurement devices and by obeying the physical constraints/laws of the producing network.

The DVR methodology is used for the first time in a multi-tier allocation system. It considers and quantifies measured and unmeasured parameters including their uncertainties in an all-encompassing production allocation scheme. The methodology also allows for measurement error diagnostics that can be integrated in a condition-based measurement maintenance program.
 

Virtual Metering System Application in the Ceiba Field, Offshore Equatorial Guinea (SPE 2011)

Virtual Metering System Application in the Ceiba Field, Offshore Equatorial Guinea (SPE 2011)

This paper presents a pilot study in the Ceiba Field that applies data validation and reconciliation (DVR) to well-rate surveillance when direct measurements are missing or unreliable. The method combines field data, uncertainty, and physical models to reconcile sensors and estimate oil, water, and gas rates, while identifying biased/failing measurements and quantifying confidence. Reported benefits include reduced well-test downtime, earlier issue detection, 65% less effort to validate well tests and allocate well rates, and better cost control through hourly volume estimates; the paper summarizes status, lessons learned, KPIs, and concludes the pilot met its success criteria.

Paper on OnePetro

 

Authors

Anton Petukov, Luigi Saputelli, Jeffrey Hermann, Alex Traxler, Kim Boles, Obiageli Nnaji, Bruno Vrielynck, Deepak Venugopal – SPE Digital Energy Conference and Exhibition – The Woodlands, Texas – 19-21 April 2011 – SPE-144197-MS
 

Abstract

Well rate surveillance is essential for reservoir characterization and selecting potential activities to enhance and optimize production. However, such variables usually lack consistent or direct field measurements, which is related to technology availability, equipment reliability, and cost control. As a result, many technologies have been developed to estimate well rates from indirect measurements (e.g., virtual metering or soft sensors).

The well rate estimation requires consistent pressure-volume-temperature (PVT) data, fit-for-purpose production well tests, and reliable sensors. In most cases, field data are used to ‘tune’ data-driven models. Missing, biased, or failing sensors may break the rate estimation, and a new calibration would be required. In addition, sensor input uncertainty and rate estimation confidence were commonly overlooked in previous approaches.

This paper discusses the implementation of a data validation and reconciliation pilot study in the Ceiba Field to estimate well rates. In this case, data, uncertainty, and models are combined to minimize a global error function. Rigorous statistics are used to calculate new sensor estimates. Unlike previous well rate estimation approaches, field-collected data are validated and corrected using physical models.

The pilot technical scope included calculating oil, water, and gas rates for each well; calculating the tolerance of rate measurements and gauge readings; and identifying sources of unreliable measurements. Although the approach is not new in the petrochemical industry, the application is ‘young’ in the upstream.

Project benefits included less downtime due to well testing and early problem detection, 65% less time expended on validating well tests and allocating individual well rates, and improved cost control due to calculating well-produced volumes hourly. These findings provide a better understanding of reservoir and well performance, which facilitates production optimization management. This paper presents a summary of current project status, the lessons learned during pilot implementation, and the procedure for further progression.

Project success criteria, application key performance indicators (KPIs), and expected benefits are reviewed and analyzed. As far as they can be evaluated at this stage, they were all achieved successfully.

 

Improving Operations Through Increased Accuracy of Production Data (SPE 2009)

Improving Operations Through Increased Accuracy of Production Data (SPE 2009)

This paper shows that Advanced data validation and reconciliation (DVR) can significantly improve production data quality in integrated oil fields, reducing uncertainty that limits production optimization and recovery efforts. By reconciling flows, pressures, temperatures, and compositions across the network, operators can obtain reliable oil, gas, and water rates even for wells without multiphase meters, often with uncertainty below 10%.

Beyond rate estimation, DVR creates many virtual measurements, flags faulty sensors, and enables condition-based, predictive maintenance. Already proven in downstream and increasingly used upstream, this digital approach helps operators better understand asset performance, optimize injection and production strategies, and expand field capacity cost effectively.

Paper on OnePetro

 

Authors

U. Wising; B. Kalitventzeff, J. Campan, B. Vrielynck – SPE Offshore Europe Conference and Exhibition – Aberdeen, UK – 8-11 September 2009 – SPE-124766-MS
 

Abstract

Production Engineers have more and more information available and operating companies are trying to integrate that information and create an “integrated oil-field” with the main objectives of maximizing production and at the same time maximize recovery. One of the challenges in these types of approaches is the quality of production data. By not having accurate enough production data, the integration efforts are covered in a layer of haze, uncertainties. By combining advanced data validation and data reconciliation techniques it is possible to improve the quality of production data and quantifying all flows, temperatures, pressures and compositions throughout the production network.

This technology has been applied in the downstream area for years and are now more and more being deployed in oil and gas production. The main goal is to provide accurate flow of oil, gas and water in each production well, but it can and has been applied to water injection systems, gas fuel and flare systems, FPSO’s etc.

It has been shown that it is possible to provide accurate production data in a production network. In particular, it has been shown that even on wells that do not have multi phase flow meters, it is possible to determine the flow of oil, gas and water with an uncertainty of less than 10%. In addition it provides other types of virtual meters, in general a production network with x measurement, would have another 2*x measurements validated and reconciled. Also by comparing measured data with validated and reconciled data it is possible to draw some conclusions; for example sensors and equipment that need maintenance can be identified much earlier and maintenance can move towards conditioned based, predictive maintenance.

This is a digital technology that can help operators have a better understanding of how their assets are performing and can help in increasing recovery from each well in the production network. Injection schemes can be performed more optimally and capacity from a field can be expanded in a cost effective way.