Common Risk Segment Mapping for Cuttings Reinjection
Drilling scenarios such as infill drilling, water flood optimization, cuttings reinjection and CO2 injection often require an in-depth study of a field to determine the optimal drilling location within a set of restrictions. Quantifying the decision making around the process requires the integration of dozens of diverse datasets, often containing dynamic information and spanning a range of realizations.
Conditional Attribute Filtering for Infill Drilling Placement
Infill drilling aims to tap previously undrained reserves in a mature hydrocarbon field. By definition, infilling happens later in field life and hence involves the analysis of greater volumes and a greater diversity of a priori data. Furthermore, the economics of infill drilling can be marginal. Therefore, a rigorous quantitative decision-making process is necessary to justify the economic risk required to implement drilling and production plans.
4D Assisted History Matching—Closing the Loop
The traditional technique of manually adjusting reservoir simulation models to achieve a history match is time consuming and problematic at best; adding 4D seismic data to the equation only increases the complexity of the task. The inclusion of 4D seismic, however, results in better constrained simulation models and a greater understanding of the uncertainty inherently associated with models.
Seismic Forward Modeling in a North Sea Reservoir
Improved, flexible and faster access to critical data is vital for fully informed decision making in today’s multidisciplinary asset development teams. It is also imperative to include the highest fidelity quantitative temporal data when building reservoir models to improve development plans and maximize returns on investment.