Abstract: An integrated capacity modelling system for monitoring and controlling multiple oil and gas assets, the system comprising a data acquisition module, capable of acquiring upstream data, whereas upstream data may include information from the multiple oil and gas assets; a subsurface and surface processes simulation module, capable of simulating production and injection performance of multiple oil and gas assets from the reservoir to the sales delivery point; a data assimilation module, capable of extracting key features in the data for the purposes of calculating production capacity of multiple oil and gas assets from the acquired upstream data and from the simulation module and for building an asset capacity model thereof; a data presentation module, capable of presenting all the components of the asset capacity model of multiple oil and gas assets, wherein the presentation module is capable of presenting the asset capacity model of multiple oil and gas assets in nodes of a hierarchical structure of a pluralit

Abstract: The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology. Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths. However, ESP performance is often observed to decline gradually and reach the point of service interruption due to factors like high gas volumes, high temperature, and corrosion. The financial impact of ESP failure is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of break-downs. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. Therefore, a data-driven analytical framework is proposed to advance towards a proactive approach to ESP health monitoring based on predictive analytics to detect impending problems, diagnose their cause, and prescribe preventive action.

Abstract: The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology. Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths. However, ESP performance is often observed to decline gradually and reach the point of service interruption due to factors like high gas volumes, high temperature, and corrosion. The financial impact of ESP failure is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of break-downs. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. Therefore, a data-driven analytical framework is proposed to advance towards a proactive approach to ESP health monitoring based on predictive analytics to detect impending problems, diagnose their cause, and prescribe preventive action.

Abstract: Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Abstract: Systems and methods for optimization of real time production operations. In one embodiment, a moving time horizon based parametric model provides fast predictions for production optimization in a short-term framework. In another embodiment, multiple technologies are selected in connection with asset performance workflows that are uniquely implemented in a multi-phase approach.

Abstract: Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Abstract: The present disclosure describes systems and methods for performing multi-level reservoir history matching. At least some illustrative embodiments include a method that includes generating a first history-matched model using at least one updated model parameter derived from one or more existing model parameters, generating a set of second history-matched models by applying a probabilistic inversion to the first history-matched model, and deriving a set of third history-matched models from the set of second history-matched models. The method further includes generating dynamic simulation realization sets using each of the set of third history-matched models, ranking each of the set of third history-matched models based at least in part on the dynamic simulation realization sets, and presenting a production forecast to a user based on the highest ranked third history-matched models.

Abstract: The present disclosure presents methods and apparatuses for forecasting geological formation production data. For example, some example methods include identifying an array of parameter sets, determining a fitting error between a first set of the historical production data and modeled production data, wherein the modeled production data is obtained through execution of a simulation model based on each parameter set of the array, determining a validation error between a second set of the historical production data and extrapolated production data for each parameter set of the array, determining a combined error for each parameter set of the array based on the fitting error and the validation error, and identifying an optimal parameter set size for modeling the target geological region, wherein the optimal parameter set size is determined in reference to a minimum combined error of the combined errors determined for each parameter set of the array.

Abstract: Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Abstract: Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Abstract: The present disclosure describes systems and methods for performing multi-level reservoir history matching. At least some illustrative embodiments include a method that includes generating a first history-matched model using at least one updated model parameter derived from one or more existing model parameters, generating a set of second history-matched models by applying a probabilistic inversion to the first history-matched model, and deriving a set of third history-matched models from the set of second history-matched models. The method further includes generating dynamic simulation realization sets using each of the set of third history-matched models, ranking each of the set of third history-matched models based at least in part on the dynamic simulation realization sets, and presenting a production forecast to a user based on the highest ranked third history-matched models.

Abstract: Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Abstract: Systems and methods for optimization of real time production operations. In one embodiment, a moving time horizon based parametric model provides fast predictions for production optimization in a short-term framework. In another embodiment, multiple technologies are selected in connection with asset performance workflows that are uniquely implemented in a multi-phase approach.

Abstract: Systems and methods for optimization of real time production operations. In one embodiment, a moving time horizon based parametric model provides fast predictions for production optimization in a short-term framework. In another embodiment, multiple technologies are selected in connection with asset performance workflows that are uniquely implemented in a multi-phase approach.

Abstract: Systems and methods for optimization of real time production operations. In one embodiment, a moving time horizon based parametric model provides fast predictions for production optimization in a short-term framework. In another embodiment, multiple technologies are selected in connection with asset performance workflows that are uniquely implemented in a multi-phase approach.