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Abstract Residual Oil Saturation (Sorw) is a critical reservoir model parameter for evaluating reserves in the Greater Burgan Field. Past Sorw studies in Greater Burgan Field either looked only at core test data, or only looked at cased-hole log data. None of the past studies considered areal position, different rocktypes, or changes in remaining oil saturation with varying amounts of water sweep. This study includes analysis of Sorw from open-hole water saturation, Time-Lapse PNC data and Special Core Analysis water flood experiments. The majority of the log data in Greater Burgan Field water - swept zones are concentrated in the 3rd sand middle, 3rd sand lower and 4th sand formations. The comparisons of the results from all three methods used in the study to measure remaining oil saturation (ROS) are limited to these reservoirs. Results from these methods were remarkably consistent. All reservoir sand with extensive PNC log data showed that zones encroached by water for 22+ years tend to be at or near residual oil conditions. Measurements in the zones with water encroachment for less than 22 years have about a 50% chance of being incompletely swept. Analysis of the 22+ year data allowed reasonable ranges of Sow were estimated from this data. Investigations of ROS spatial variations in the Magwa, Ahmadi and Burgan sub-fields were made. 3rd sand middle was the only reservoir with both adequate PNC and open-hole coverage in ROS from these three areas in Greater Burgan Field. ROS by rocktype was reviewed in three categories of reservoir rock (excellent, medium and poor quality reservoir) as currently defined by log analysis in Greater Burgan Field. The vast majority of log data occurs in rocktype 1, the highest quality reservoir rock. Only 3rd sand lower formation contained sufficient data in all three reservoir quality rocktype to make valid comparison. Both core flood Tests and PNC Time-Lapse methods also showed no difference in ROS based rocktype. Introduction Background Definitions Residual Oil Saturation (Sorw) is defined as the lowest oil saturation that a reservoir can achieve (technically, a fixed value for a given reservoir and recovery mechanism). With logging tools, we can measure Remaining Oil Saturation (ROS), the oil saturation calculated from a reservoir after it is swept by water due to production. Eventually, these two oil saturations become the same. This study will show evidence to suggest that in Greater Burgan Field, the remaining oil saturation may be changing (lowering) through time, and that true residual oil conditions are frequently not met until a reservoir has been swept for many years. Commonly, the term residual oil saturation is loosely defined as both the true residual oil saturation and the oil saturation that can be measured today with a logging tool. For reserve estimations, the value of the true residual condition is used. When the topic is oil saturation as measured by logging tools, it is also commonly referred to as residual oil, even though the term remaining oil would be more precise. This report will attempt to keep the definitions of Sorw and ROS distinct. ROS Is Important To Reserve Estimation Sorw is a critcal parameter for the accurate estimation of reseves in Greater Burgan Field. Determination of residual oil saturation can provide the basis to refine predictions of a reservoir's recovery factor, and thus possibly increase a field's reserve estimates. There are many ways to measure ROS (and by analogy Sorw) in a dynamic reservoir. Unfortunately, frequently these different methods will not yield consistent answers.
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Wara Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Ratawi Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Mauddud Formation (0.99)
- (15 more...)
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.
- Asia > Middle East (0.29)
- North America > United States > Texas (0.28)
- Geology > Structural Geology > Fault (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Quality (0.74)