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.

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.

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.