Automatic updates of simulation models with historical field performance and events is a challenging and time-consuming task that reservoir engineers need to tackle; whether it is to maintain history matched reservoir models (evergreen assets), undertake new calibration exercise or update forecasting studies. The challenge takes another dimension with increasing complexity of field operations (production/injection/drilling/workover), and well designs and configuration of downhole equipment. This paper presents an efficient workflow capitalizing on IR4.0 Digital Twin principles to automate the process of seamlessly integrating and updating historical wells’ information in reservoir simulation models. The objective of this workflow is to drive reservoir simulation towards capitalizing on digital transformation and the Live Earth models concept to revolutionize model calibration and history matching for superior quality of prediction with great confidence.
Well data digitization in this workflow was achieved through automating well data acquisition, well data quality checking enforcement and well modeling in interconnected simulation applications. The workflow minimizes human manual interaction with data giving engineers the chance to focus more on reservoir engineering aspects of reservoir engineering tasks. The workflow consists of four steps. The first step is data acquisition in which various types of well data are fetched. The second step is data quality check in which data from different data sources is subjected to engineering and scientific measures (i.e. Quality Indices) that translate engineering knowledge and experience to detect possible data inconsistencies. The third and fourth steps cover exporting and importing relevant data within the reservoir simulation applications’ portfolio where various data types are handled and managed seamlessly. Data and event acquisition workflows were automated to provide seamless well data transfer between different data sources and reservoir simulation pre and post-processing applications. The different types of well data were obtained through automatic fetching from data repository (databases, petrophysical models … etc.). The Quality Check (QC) procedures were automatically performed against deviation surveys, perforations, casing/tubing, flowmeter, cores, formation tops and productivity/injectivity index. This helped in identifying data discrepancy, if any, including missing data entries and contradicting well events.
The automation of these workflows significantly reduced the time needed for well data transmission/update to the reservoir models, eliminated human errors associated with data entry or corrections, and helped keeping the models up-to-date (evergreen). Incorporating the digital twin concepts enabled advanced automatic digitization of well information. It provided a data exchange solution that meets E&P requirements and provided more effective and efficient methods of connecting diverse applications and data repositories.
