When implementing any field development plan or an Enhanced Oil Recovery (EOR) project, it is critical to understand the key variables that influence the success of the plan such as; reservoir parameters and fluid properties. Mature Oil & Gas fields' increasing complexity of recovery mechanisms dictates an improved understanding of the fields' behaviour and the technologies that must be applied to maintain and prolong oil production plateau and achieve ultimate recovery potential.
History matching forms an integral part of the reservoir modeling workflow process. It is used to examine the field performance under different production and injection scenarios in order to select the best scenario for hydrocarbon production. However, the history-matching process can be very frustrating and time-consuming, even for fields that appear relatively small and simple in nature, because of the reservoir processes involved and the non-unique nature of the solution.
Traditionally, history matching is conducted as a deterministic process with a single realization considered representative at a single point in time. Although, the input data usually go through a data analysis process where the major uncertainties and scenarios are defined, and uncertainty ranges are created, time and budget constraints usually result in significant reductions in the number of sensitivity runs and analysis for the input data validation and quality control that results in an incomplete investigation of the uncertainty quantification. Therefore, uncertainties inherited in the Petrophysical data are carried from the static model construction throughout the entire dynamic modelling process, ultimately leading to less-than-optimal models to be used as a decision making tool.
Consequently, due to the non-uniqueness of the numerical solution, a good history-matched model might have geological and petrophysical properties quite far from those of the "Field?? and therefore could lead to a bad forecast. As in any numerical model, petrophysical data quality is fundamental for model precision.
A zero offset VSP survey was acquired over a thick carbonate interval. The objectives of the repeat survey were to assess repeatability of the measurement, robustness of the time lapse VSP processing mainly to account for time lapse noise and quantification of the time lapse signal in the reservoir due replacement of water by gas caused by gas injection that occurred between the baseline and repeat surveys.
A long side of the two VSP's, an array sonic (SDT) and the sonic scanner tools were run. The SDT was run to recover the monopole compresssional and shear waves, while sonic scanner to acquire both the dipole and monopole shear and compressional.
Data processing was focused on isolating the time lapse signal in the reservoir section by comparing the baseline and repeat VSP surveys and sonic logs. Both the baseline and repeat surveys were processed with identical parameters to ensure that the datasets are comparable at every processing step. The processed data shows a good degree of repeatability, and deterministic deconvolution as expected was enough to take care of source wavelet variations between the two surveys.
The time lapse VSP and sonic logs were run in carbonate reservoirs where gas is being injected to assess the effectiveness of recovering more oil as part of Enhanced Oil Recovery (EOR). The current recovery mechanism in the reservoirs is to inject water to enhance pressure support. The gas pilot objective is to increase the recovery factor to recover more oil and enhance productivity.
In the past ten years, time-lapse (4D) seismic has evolved from an academic research topic to a standard way of monitoring reservoir performance. The method is now being used as good reservoir management practice to provide evidence of saturation changes within the reservoir at field scale. 4D provides a new piece of data describing the dynamic behavior of the reservoir fluids between the wells, often limited to small scale monitoring at the borehole scale. Thus, it provides sophisticated techniques of reservoir monitoring and management relying on the integration of geological models, static and dynamic properties of the reservoir rock, and detailed production and pressure field data.
While 4D seismic data has been very successful in monitoring hydrocarbon production in clastic reservoirs, there is still no consensus on its applicability to carbonate fields. The main difficulty is the well-known fact that the acoustic velocities of carbonates are insensitive to saturation and pressure changes, relative to the clastics. Beside the geological processes such as production induced compaction which has large impact on porosity, density and permeability variation during the life of a 4D surveys, the complexity and heterogeneity of carbonate pore geometry and network further aggravate the difficulty of 4D applications. Although the geological characteristics may not change at small time scales but they are linked to fluid flow and distribution in the reservoir.
An Integrated approach of 4D seismic analysis using all historical production data along with open/cased hole logs and simulation models has demonstrated its viability to understand saturation changes in heterogeneous reservoirs. Thus using this approach as reservoir management tool to improve sweep efficiency and enhance field development plans.