Thermal recovery processes are widely applied for heavy oil and bitumen production. Unique thermal properties of water and water steam allowed efficient reduction of extremely high viscosities by several orders of magnitude and made a vast heavy oil and bitumen reserves production technically and economically feasible. Steam effect on heavy oil and bitumen in traditional reservoir engineering for a long time has been considered as physical only, i.e. viscosity reduction, improved flow parameters, distillation effects, emulsification, etc. However multiple laboratory studies and field observations suggest that initial oil undergoes chemical alteration and gases such as H2S and CO2 could be produced in increased quantities. Estimation of H2S and CO2 production potential is important due to considerable corrosivity of these gases, associated environmental, economical and other issues. In this study a practical approach has been developed to simulate and forecast H2S and CO2 production during thermal recovery using common reservoir simulation tools. First, analytical data was matched and then chemical reaction had been implemented to the sector model. Steam Assisted Gravity Drainage (SAGD) was chosen to demonstrate the concept of suggested approach and analyze the results. Generated gases were considered to be soluble both in water and oil. The importance of accounting for gas solubility in water was demonstrated and discussed. Simulated volumes of H2S and CO2 are in good agreement with that observed in the field applications of steam assisted recovery methods.

This paper reports on a study with the objective to validate a set of core analysis data using a combination of mercury injection capillary pressure (MICP) data and statistical correlation techniques. The data set is from an off-shore reservoir in Atlantic Canada. Analysis of this reservoir was complicated by the fact that the permeabilities of the samples were high, greater than 2400 mD. The analysis was done using an existing data set, not a data set specifically tailored for the techniques used in the analysis. The data analyzed included samples that represented seven zones in a single well. Porosities and permeabilities were available for the MICP samples. Electrical properties, along with porosities and permeabilities, were available on samples from each zone, but not from the same depths as the MICP samples. Steady-state relative permeabilities (SSRP) were available for stacked samples in each zone; one of the samples in the stack was a companion sample for one of the MICP samples from that zone. The MICP results were used to validate the permeability measurements using both the Swanson method (SM) and the Ruth-Lindsay-Allen (RLAM) method. The SM, using published correlation parameters, significantly under-predicted the permeabilities; the RLAM, which uses no correlation parameters, gave predictions within a maximum error of just over 33% and a mean error of -12%. The MICP data was used to validate the shapes of the SSRP curves using the Gates and Tempelaar-Lietz method (GT-LM), the Burdine method (BM), and a modified Burdine method (MBM). The GT-LM, which uses no correlation parameters, provided good predictions of the wetting phase SSRP curves but very poor predictions of the non-wetting phase SSRP curves. The BM, using published correlation parameters, provided poor predictions of the wetting phase SSRP curves but improved predictions of the non-wetting phase SSRP curves. The MBM provided good predictions of the wetting phase SSRP curves and acceptable predictions of the non-wetting phase SSRP curves. The MBM method does use a correlation parameter but a single value was used for all seven zones. This work provides a protocol for validating core analysis data that can be implemented in a straightforward manner to determine the “quality” of the data. The results emphasize the importance of MICP as an experimental technique. A proposed modified workflow is presented that would optimize the validation protocol.

Rock and Fluid data provide critical input parameters for reservoir characterisation, production, and reserve forecasts. These datasets are key building blocks for reservoir simulation models, but there has been limited work in the oil industry on the standardisation of their data structures, formats, or quality control. We will present experiences and results from an ongoing digitalisation initiative. This initiative centres on establishing a database that includes all relevant analysis done on rock and fluid data. The database currently has thousands of wells covering over a hundred NCS fields that can be easily accessed via standard interfaces. As the data are contextualised on e.g., lithostratigraphic units, end-users can analyze data by groups, formation, geological time, and depositional environment. A key challenge for rock and fluid data compared to simpler oil industry datasets, is that one discipline's analysis and modelling results represent another discipline's input data. We will present an approach to resolve these challenges and discuss how this relates to ongoing joint standardisation efforts in the industry such as the OSDU Forum. In this paper we will show how industrial DataOps technology and close collaboration between domain experts, data managers and technologists has unlocked great value based on Rock and Fluid data. We will describe the workflow of finding and verifying data, establishing standard formats, implementing automated data validation and ingestion, and how we manage, visualise, and interpret data, ensure full data integrity, and make data available for end-users through different applications. We will provide examples on how machine learning can be used for automated trend analysis and identification of relationships across different data types to support model generation. Finally, we will demonstrate a state of the art and seamless workflow of generating static and dynamic SCAL model input for reservoir simulation with uncertainty band. This includes evaluation and verification of both static and dynamic SCAL data. Relative permeability and capillary pressure curves from each laboratory experiment are quality assured, parameterised and stored in the database, along with all relevant information such as plug properties, well name, fluid properties, experimental conditions, flow parameters and endpoint saturations etc.