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Real-time production surveillance and optimization requires availability of predictive well models with reasonable accuracy that can estimate production rates in response to change in operating conditions. These models that are mostly physics based are parameterized in terms of gas oil ratio (GOR), water cut (WC), productivity index (PI), and reservoir pressure. Further, the models are tuned using measured data namely production rates of oil, water, gas, and pressure measurements such as downhole gauge pressure, wellhead and flowline pressures that are captured during a well test. For mature oil fields where, well tests are infrequent or sensors start malfunctioning, relevant data required for model tuning is no longer available. In absence of updated models, real-time predictions tend to deviate with time compared to the observed data resulting in less reliable well rate allocation and production optimization recommendations, if any. This work describes a regression-based technique and its application for predicting quantitative as well as directional changes in well parameters between well tests that can be used to improve well allocations. A regression method that estimates well parameters while minimizing a non-linear least square loss function derived from deviation between measured and model-based estimates of rate and pressure data is implemented. The method can estimate well parameters for both an individual and multiple wells simultaneously while solving for a network of wells connected to production separator using IPM GAP. The application of the method to a subsea asset is demonstrated while evaluating its performance for different scenarios comprising variation in number of wells and well parameters. Additionally, the capability of the method to predict directional changes in well parameters is demonstrated by validating it against historical data. The estimates from regression method were found within 5% difference compared to well parameters obtained from multiphase flow meters for the scenarios where number of wells and the number of regression parameters per well were limited. This difference increased with increase in number of wells and number of regression parameters per well owing to the fact that solution space expands with increased degrees of freedom. However, the directional changes in parameters were predicted accurately when looked at larger time scales. It was inferred that the application of regression method is best suited for the scenarios where well parameter estimations are needed for a limited number of wells and the parameters for the remaining wells in a network are representative. Additionally, availability and reliability of sensor data largely impacts the method outcomes.
Real-time production surveillance and optimization requires availability of predictive well models with reasonable accuracy that can estimate production rates in response to change in operating conditions. These models that are mostly physics based are parameterized in terms of gas oil ratio (GOR), water cut (WC), productivity index (PI), and reservoir pressure. Further, the models are tuned using measured data namely production rates of oil, water, gas, and pressure measurements such as downhole gauge pressure, wellhead and flowline pressures that are captured during a well test. For mature oil fields where, well tests are infrequent or sensors start malfunctioning, relevant data required for model tuning is no longer available. In absence of updated models, real-time predictions tend to deviate with time compared to the observed data resulting in less reliable well rate allocation and production optimization recommendations, if any. This work describes a regression-based technique and its application for predicting quantitative as well as directional changes in well parameters between well tests that can be used to improve well allocations. A regression method that estimates well parameters while minimizing a non-linear least square loss function derived from deviation between measured and model-based estimates of rate and pressure data is implemented. The method can estimate well parameters for both an individual and multiple wells simultaneously while solving for a network of wells connected to production separator using IPM GAP. The application of the method to a subsea asset is demonstrated while evaluating its performance for different scenarios comprising variation in number of wells and well parameters. Additionally, the capability of the method to predict directional changes in well parameters is demonstrated by validating it against historical data. The estimates from regression method were found within 5% difference compared to well parameters obtained from multiphase flow meters for the scenarios where number of wells and the number of regression parameters per well were limited. This difference increased with increase in number of wells and number of regression parameters per well owing to the fact that solution space expands with increased degrees of freedom. However, the directional changes in parameters were predicted accurately when looked at larger time scales. It was inferred that the application of regression method is best suited for the scenarios where well parameter estimations are needed for a limited number of wells and the parameters for the remaining wells in a network are representative. Additionally, availability and reliability of sensor data largely impacts the method outcomes.
Shut-ins of production and injection wells are an integral part of annual asset planning to obtain information that is unavailable during production well tests or routine injection operations. Near-wellbore reservoir pressures, productivity and injectivity indices, and skin factors are critical for production, injection and maintenance schedule planning and can be estimated using pressure data observed during well shut-ins. Engineers across different assets often utilize spreadsheet-based tools with custom written macros to acquire and analyze relevant data and to estimate well parameters. These user-developed tools can be field or well-specific and lack generalization for adoption across multiple assets, proving difficult to maintain and lacking proper data archival mechanisms that are crucial to track evolution of well behavior over time. In an effort towards digitalization, a solution that automates identification, validation and archival of well shut-in periods with asset and completion specific rules has been developed. A graphical user interface (GUI) provides visualization of relevant pressure, temperature, rate, and valve position attributes during shut-in periods and enables the necessary user interactivity to analyze shut-in periods. The digital solution integrates application programming interfaces (APIs) to retrieve real-time data from OSISoft PI servers and to store identified shut-in periods and associated attributes in centralized SQL databases. Operating valve positions, rates, and choke openings are tracked to approximate shut-in periods with configurable rules through JSON interfaces. Furthermore, filter convolution is applied on pressure gradient data to fine tune shut-in start and end times. The programmatic methodology leverages valve position trends, in addition to wellhead and downhole pressures and temperatures observed during each recorded shut-in period to facilitate shut-in validation. Dashboards comparing pressure build-ups for producers and pressure falloffs for injectors across different shut-in periods captured during a well's operational history enable engineers to identify potential changes in skin effects, permeability, and wellbore phase redistribution effects. To facilitate estimation of reservoir pressures using build-up/fall-off data, a reservoir pressure estimation method has been implemented with a built-in user interface to update and archive changes in wellbore reference depths and gradients which are vital for depth correction of pressure data. Steady-state detection enforces validation criteria for stable pressure before shut-in and rapid choke ramp-down, augmenting simpler QA/QC rules such as minimum flowing duration between shut-in periods and time taken to reach pressure differential thresholds. The integrated digital solution has been deployed as a web application to several oil-producing assets. The application is expected to aid asset engineers in planning and validating shut-ins, and to standardize extraction and archival of additional wellbore and reservoir information efficiently. This presentation highlights the development of programmatic components for the described methodology and demonstrates an application of the tool to analyze shut-ins for a deepwater field.
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