Lessons Learned from Static Reservoir Modelling on Complex Carbonate Fields, Onshore UAE

Abstract

Abstract Commercially available software packages used to develop geocellular models are becoming increasingly user friendly. However, workflows for data quality control, modelling procedures and evaluation of results are not always well established, tempting inexperienced users to build 3D models without the necessary rigor. The economical and technical implications of using 3D models on reservoir simulation studies, volumetrics, and field development, on the other hand, are of paramount importance. The need to adopt methodical and systematic processes during all stages of 3D model building and dynamic simulation is now widely recognized. This paper compiles some key data flow, process flow and quality management practices. It presents the chain of activities in 3D model building and usage, highlighting critical parts of the process, rules of thumb for obtaining quality results, best practices to be adopted, pitfalls in various assumptions and some of the prevalent misconceptions. Issues addressed include: data analysis; quality control; data density; integration of disparate data types; framework modeling; incorporation of chrono-stratigraphic zonation; fault and fracture modeling; deterministic and stochastic property modeling; depth and property uncertainty analysis; upscaling, downscaling and visualization. Experience on large and diverse carbonate fields, in a variety of conditions and different stages of maturity, presenting different sets of data types and well densities are combined to present a summary of useful hints and best practices to other professionals involved in doing static modelling. It is emphasized that if data QC, geologic rules, mapping principles and geostatistics are not handled properly, the resulting model will be less precise, regardless of the sophistication of the software and algorithms deployed. Introduction This paper compiles experiences and practices gathered by several professionals regarding the static modeling of complex carbonate reservoirs. It aims to be useful in helping building robust and optimal models which are precise and accurate for geological usage and reservoir simulation. It also addresses the need for reference documentation of modeling processes to professionals from various levels of expertise. The key data flow, process flow and quality management practices correspond to the complete sequence of activities in 3D model building and usage, highlighting critical parts of the process, rules of thumb for obtaining quality results, best practices to be adopted, pitfalls in various assumptions and prevalent misconceptions. The quality, accuracy and consistency of the database are also stressed as one of the most significant issues in building any 3D geological model. Experiences on carbonate fields in a variety of conditions and stages of maturity, possessing different data types and well densities are combined with deterministic and stochastic property modeling; depth and property uncertainty analysis; upscaling and downscaling, and visualization to present a compendium of useful hints for professionals involved in static modeling. Data Quality Checking One of the main rules when performing geological modelling is to quality checking the data as well as the results at every step of the process. In fact, every professional involved on the data transfer to the geomodeller should be responsible for the quality of the delivered data. The responsibility for the correction of invalid data identified during the quality checking (at any step of the modeling workflow) should rest with the data originator. In addition, the data originator should as well be responsible for keeping the database content up to date. Data management is the key for reliable geological models and when invalid/missing data are identified at an early stage work can be saved.