Short-Term Production Forecasting to Evaluate Production Target Deliverability Using End to End Integration Platform from Reservoir to Surface Network

Abstract

Forecasting oil and gas production for a well or reservoir is one of the most valuable tasks of a reservoir engineer. This paper elaborates on the assessment of production targets deliverability using a dynamic and integrated approach to perform short term production forecasting. The case also studies the seamless integration of sub-surface with well and facility network models providing options to examine the feasibility of production plans. The principal approach employed in the methodology comprises an automated workflow, which includes reservoir simulation data, wells, and network models enclosed in a dynamic loop, where workflow iteration takes place until the production target is achieved. Within this implementation, the process allows the estimation of short-term production forecasts mainly used for optimizing production operations and business planning, among other tasks. Some of the main steps followed in order to assess the feasibility of the production targets are:

Well, Network and Reservoir data QA/QC and further alignment

Narrowing down of gaps between the surface and sub-surface system

Integration among the several data-driven sources

Iteration of the overall process allowing minimal human intervention

Throughout this implementation, it was clearly appreciated that production forecasting represents a highly complex task due to the number of different components included in an integrated system and their intrinsic interconnection, where essentially every piece of the calculation influences others. The case study highlighted how performing a dynamic reservoir integration run in an integrated digital production system can help engineers to provide a way to check the feasibility of short-term production targets while considering full surface system configuration. Moreover, the integrated production system provided flexibility in terms of setting up forecast scenarios in an efficient manner, thereby minimizing users' time and efforts in data handling and driving maximum user focus on results and analysis. A dedicated forecast server helped in achieving run performance, thereby enabling the user to carry out various what-if scenarios in a short amount of time. The case studies also discuss a few key challenges encountered during the process that represented a difficulty in overcoming unless addressed in an integrated collaborative system:

Data size and complexity

Lack of data and/or data inconsistency

Surface and Sub-surface model configuration for dynamic integration

Gaps between surface and sub-surface performances at initial time step.

The application of this integrated and automated workflow approach improved confidence in the reservoir target deliverables by providing robust data management and better predictions resulting from evaluating the entire system (including the performance of wells and reservoirs at the same time). This helped in saving user analysis time significantly by avoiding the process of analyzing all the sections of the system in isolated silos, which is usually the approach followed by many operators with large amounts of wells.