TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir simulation has become the industry standard for reservoir management. It is now used in all phases of field development in the oil and gas industry. The full field reservoir models that have become the major source of information and prediction for decision making are continuously updated and major fields now have several versions of their model with each new version being a major improvement over the previous one. The newer versions have the latest information (geologic, geophysical and petrophysical measurements, interpretations and calculations based on new logs, seismic data, injection and productions, etc.) incorporated in them along with adjustments that usually are the result of single-well or multi-well history matching. A typical reservoir model consists of hundreds of thousands and in many cases millions of grid blocks. As the size of the reservoir models grow the time required for each run increases. Schemes such as grid computing and parallel processing helps to a certain degree but cannot close the gap that exists between simulation runs and real-time processing. On the other hand with the new push for smart fields (a.k.a. ifields) in the industry that is a natural growth of smart completions and smart wells, the need for being able to process information in real time becomes more pronounced. Surrogate Reservoir Models (SRMs) are the natural solution to address this necessity. SRMs are prototypes of the full field models that can run in fractions of a second rather than in hours or days. They mimic the capabilities of a full field model with high accuracy. These models can be developed regularly (as new versions of the full field models become available) off-line and can be put online for automatic history matching and real-time processing that can guide important decisions. SRMs can efficiently be used for real-time optimization, real-time decision making as well as analysis under uncertain conditions. This paper presents a unified approach for development of SRMs using the state-of-the-art in intelligent systems techniques. An example for developing an SRM for a giant oil field in the Middle East is presented and the results of the analysis using the SRM for this field is discussed. In this example application SRM is used in order to analyze the impact of the uncertainties associated with several input parameters into the full field model.

Polymer flooding is a mature EOR technique successfully applied commercially in sandstone reservoirs and at the pilot stage in carbonate reservoirs. However, all previous pilots in carbonates reservoirs were implemented in relatively low temperature and low salinity conditions. No field application of polymer in carbonate was implemented in the last 25 years. In recent EOR screening studies for carbonate reservoirs in Abu Dhabi, polymer based EOR techniques were identified to target by-passed oil in heterogeneous/layered reservoirs The main challenges for polymer based EOR processes in ADNOC reservoirs is to find a stable polymer under the extreme conditions of high temperature/high salinity/high content of divalent cations which can be injected in carbonate reservoirs. An extensive laboratory program initiated 10 years ago led to the development of a polymer rich in Sodium Acrylamido tertiobutyl Sulfonate (ATBS). Thermal stability, bulk and in-situ rheology, adsorption and injectivity performed and the polymer was found suitable for the harsh conditions of ADNOC reservoirs. A de-risking strategy was designed in which a polymer injectivity test (PIT) followed by a multi well pilot are performed before the full field implementation of polymer based EOR for a number of ADNOC reservoirs. This paper describes in details the main steps of the successful PIT recently carried out including: the selection of the candidate well and the injection skid, the test design, its execution, the polymer solution quality management and the operational challenges faced during the pilot. The polymer Injectivity Test was conducted for 4 months and concluded by February 2020. A total of 150,000 barrels of viscous solution was successfully injected into the reservoir. This paper also details the real time surveillance and injection monitoring plans implemented during the test period for real time assessment of the skid delivery and the well response. This Injectivity test achieved the designed Key Performance Indicators related to polymer solution quality, viscosity, concentration, injection rate and skid running time. The dedicated surveillance and injection monitoring plan designed and implemented during this pilot, enables to confirm the good performance of the polymer during PIT period. Furthermore, PIT results showed good performance of Polymer in terms of viscosity, Injectivity at target rate and concentration. This paper also addresses the impact of water quality on polymer viscosity and skid operation. This paper presents field results for a new polymer developed for carbonate reservoirs at HT/HS. This successful Polymer Injectivity test qualified the new polymer for field application at harsh carbonate reservoir conditions. Results from this world first Injectivity test opens a new area of possibilities to improve recovery in giant heterogeneous carbonate reservoirs in ADNOC and in the Middle East.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir simulation has become the industry standard for reservoir management. It is now used in all phases of field development in the oil and gas industry. The full field reservoir models that have become the major source of information and prediction for decision making are continuously updated and major fields now have several versions of their model with each new version being a major improvement over the previous one. The newer versions have the latest information (geologic, geophysical and petrophysical measurements, interpretations and calculations based on new logs, seismic data, injection and productions, etc.) incorporated in them along with adjustments that usually are the result of single-well or multi-well history matching. A typical reservoir model consists of hundreds of thousands and in many cases millions of grid blocks. As the size of the reservoir models grow the time required for each run increases. Schemes such as grid computing and parallel processing helps to a certain degree but cannot close the gap that exists between simulation runs and real-time processing. On the other hand with the new push for smart fields (a.k.a. ifields) in the industry that is a natural growth of smart completions and smart wells, the need for being able to process information in real time becomes more pronounced. Surrogate Reservoir Models (SRMs) are the natural solution to address this necessity. SRMs are prototypes of the full field models that can run in fractions of a second rather than in hours or days. They mimic the capabilities of a full field model with high accuracy. These models can be developed regularly (as new versions of the full field models become available) off-line and can be put online for automatic history matching and real-time processing that can guide important decisions. SRMs can efficiently be used for real-time optimization, real-time decision making as well as analysis under uncertain conditions. This paper presents a unified approach for development of SRMs using the state-of-the-art in intelligent systems techniques. An example for developing an SRM for a giant oil field in the Middle East is presented and the results of the analysis using the SRM for this field is discussed. In this example application SRM is used in order to analyze the impact of the uncertainties associated with several input parameters into the full field model.