A case study on the integration of 4D seismic with various multi-scale and multidimensional field data to understand dynamic behaviour of the reservoirs is presented. 4D seismic is a key dataset amongst others e.g.geochemical fingerprinting, well inflow tracers, injection logging tools/production logging tools, and multi-well pressure deconvolution) together withconventional field data, which is acquired since starting up the field in late-2016. 4D data proved to be an essential piece that complemented field observations and is integral for constraining the subsurface models in support of a rapid second pahse of development and WRFM decisions. The paper describes the approaches taken to integrate these distinct datasets in the dynamic model as well as the various challenges faced in assimilating them in a coherent manner. One key subsurface challenge is to understand the degree of compartmentalisation risk to make sound WRFM decisions and to plan for a robust phase 2 development. As a starting point, conventional field performance analysis (production & injection performance) indicated connectivity across the reservoirs, though more limited in certain areas. This was supplemented with other subsurface data to further validate and improve the dynamic models. The 4D signals provided an indication of pressure and fluid connectivity as well as an indication of water sweep direction. Updates to the dynamic fault seal were performed in line with observations from 4D seismic and various field data. Understanding the dynamic behavior of the M field is key in view of the various challenges faced in reservoir management, e.g. increasing GOR trends and lower WI performance, in parallel with developing plans for the Phase 2 development. The incorporation of data of different scales and dimensions (4D seismic, fluid chemistry, PLT, multi-well pressure deconvolution) added value to the process of updating the dynamic models.

The paper intends to share the ideas and methodology on how to improve history matching and manage uncertainties by integrating the deterministic method and stochastic Experimental Design (ED) approach in the most practical way. The integrated method was applied in the re-development study of an offshore clastic oil Field. In the conventional deterministic approach, history matching is conducted by manually tuning the key parameters to achieve pressure and saturation matching iteratively. The main advantage of the deterministic approach is in providing the opportunity to understand the details of the well and reservoir local behaviors during the history matching exercise. The disadvantages are the workflow yields only a single outcome hence poses difficulty in quantifying the effect of multiple uncertainties and is dependent on the of engineer's subjective. Meanwhile, history matching stochastic workflow assisted by ED is a more systematic procedure built to capture the effect of uncertainties on the range of outcomes. However, the parameters used in the stochastic process is typically of global scale, thus the engineer may lose insight into the localized reservoir behaviors. Furthermore, proxies built in ED, although statistically consistent; do not always conform to the reservoir physics as most of the reservoir mechanisms are too complex for reliable modeling by ED-based proxy equations. Candidate Field re-development study aimed to further improve recovery by infill drilling. Analysis of well production data suggested that the remaining oil was entrapped to the crestal region of the field. Full field simulation work was then conducted to target and quantify the infill opportunity. The integrated approach began with a structured deterministic history matching adopting the well-known stratigraphic method. The deterministic history matching involves mostly local modifications of the aquifer strength, fault sealing and well's PI multiplier to achieve satisfactory match on the well and reservoir level. This was then used as the starting base case for the ED workflow. ED History Match led to multiple best matched cases, which were then run in forecasting mode and yielded a range of outcomes. ED History Matching also proved to improve the match quality of the initial deterministic history matched model. Sweet-spotting exercise was performed based on multiple History Matched models and suggested 3 infill potential locations in the major reservoirs, providing three concepts of infill drilling: unpenetrated fault blocks, crestal oil accumulation and sub-optimum drainage area. After considering the risk and confidence level of each potential location, the crestal oil accumulation was selected as the most attractive. Having multiple best match models helped to provide the range of incremental UR that is consistent with the range of uncertainties. The study demonstrated that integration of deterministic and stochastic history matching method has been instrumental in managing uncertainties and identifying redevelopment opportunities of a mature waterflood field.