Simulation of reservoir flow processes at the finest scale is computationally expensive and in some cases impractical. Consequently, upscaling of several fine-scale grid blocks into fewer coarse-scale grids has become an integral part of reservoir simulation for most reservoirs. This is because as the number of grid blocks increases, the number of flow equations increases and this increases, in large proportion, the time required for solving flow problems. Although we can adopt parallel computation to share the load, a large number of grid blocks still pose significant computational challenges. Thus, upscaling acts as a bridge between the reservoir scale and the simulation scale. However as the upscaling ratio is increased, the accuracy of the numerical simulation is reduced; hence, there is a need to keep a balance between the two. In this work, we present a sensitivity-based upscaling technique that is applicable during history matching. This method involves partial homogenization of the reservoir model based on the model reduction pattern obtained from analysis of the sensitivity matrix. The technique is based on wavelet transformation and reduction of the data and model spaces as presented in the 2Dwp-wk approach. In the 2Dwp-wk approach, a set of wavelets of measured data is first selected and then a reduced model space composed of important wavelets is gradually built during the first few iterations of nonlinear regression. The building of the reduced model space is done by thresholding the full wavelet sensitivity matrix. The pattern of permeability distribution in the reservoir resulting from the thresholding of the full wavelet sensitivity matrix is used to determine the neighboring grids that are upscaled. In essence, neighboring grid blocks having the same permeability values due to model space reduction are combined into a single grid block in the simulation model, thus integrating upscaling with wavelet multiscale inverse modeling. We apply the method to estimate the parameters of two synthetic reservoirs. The history matching results obtained using this sensitivity-based upscaling are in very close agreement with the match provided by fine-scale inverse analysis. The reliability of the technique is evaluated using various scenarios and almost all the cases considered have shown very good results. The technique speeds up the history matching process without seriously compromising the accuracy of the estimates.

This paper explains the importance for implementation of early water flood in near saturation pressure oil reservoirs particularly for the case having solution gas as dominant drive mechanism. The depletion in case of solution-gas drive (having no or minor support) with low to moderate in-place volumes is relatively fast. It is commonly observed that no pressure maintenance program is implemented till the reservoir pressure has been severely exhausted. This delay is generally caused by time consumed during understanding of fluid and reservoir behavior, and ultimately symbolizes the phrase ‘missing the train’. The objective of this study is to present the importance of early water flood and its impact on oil recoveries. A field was discovered in South Indus Basin which has half graben and four-way fault bounded structure with numerous splay faults. A well was drilled which encountered Sands ‘A’ and initially produced ~1360 bopd having ~4500 psia initial reservoir pressure. A detailed study was carried out when the reservoir pressure had depleted from 4500 to 1200 psia after draining ~400 MBO with ~1 Bscf associated gas. Based on the outcomes of the study, water flood was implemented by drilling an injector well ‘Inje-1’ which increased the pressure from 1200 psi to 4500 psi in the later life of field. Despite the pressure had rose to initial reservoir pressure, the recovery from the reservoir remained sub optimal. To understand the importance of implementing early water flood at higher pressures, a numerical simulation model was developed, history matched, and various sensitivities were run to see the impact of water flooding at various reservoir pressures during the life span of the field. It was observed that the recovery would have been more if the water flooding was implemented when the reservoir pressure was above bubble point. The reason being liberation of gas and shrinkage of oil resulting in high viscosity and low mobility oil remaining behind. If this liberation of gas is prevented by injecting water and conserving reservoir energy, both viscosity and mobility of oil would remain favorable due to delay in arriving at saturation conditions. Hence the recovery of these types of reservoirs can be enhanced by taking advantage of low viscosity and higher mobility of oil during early life. If the waterflood is implemented after exhausting the reservoir pressure, then the increased viscosity restricts oil flow and causes water channeling due to higher mobility contrast. As a result, leaving behind bypassed oil zones and very high residual oil saturation. In the present case study, it was observed that if the water flooding was implemented prior to reaching bubble point, recoveries would be 7-15% higher as compared to previous recovery. The early implementation would have added value to the overall project. Implementing the lesson learned, recent new discoveries are being evaluated to initiate water flood in early life. Early implementation of water flood in the oil reservoirs closed to saturation pressures will always be beneficial. Appropriate field development plan of the field and right decisions at right time will aid to enhance oil recovery. Once the energy in the oil reservoir is drained after producing gas, it is very difficult to regain the same energy.

This paper focuses on production forecasting of a tight oil reservoir using numerical sector modeling technique and its advantages over conventional decline curve analysis (DCA) and material balance methods. Sector modeling has resulted in better control over prediction as compared to the actual performance. This is mainly due to better understanding and handling of vertical reservoir heterogeneity and pore pressure depletion, where in, the later is treated as an implicit constant thereby falling short of capturing pore pressure changes and resulting impact over well rates and drainage areas. A well was drilled in the South Indus basin to explore, assess, and produce hydrocarbons from cretaceous age sandstone reservoirs. This field has a complex structure due to multiple splay faults. The well logs indicated hydrocarbon and perforations were carried but the well did not flow naturally due to tight nature. Hydraulic frac was executed and well was commissioned on Artificial lift and produced at optimized rate of ~180 Bopd. A pressure buildup survey was also carried out which indicated ~1 mD permeability. The objective of this study was to determine the reliable tool for prediction of tight oil fields and evaluate future development scenarios. Analytical approaches including conventional Material balance & DCA as well as numerical simulation technique was adopted for performance prediction. Therefore, material balance and DCA was performed based on available data, and subsequently, a sector model was developed using commercial simulator. Sufficient structure around the wellbore was imported in the static modeling package wherein the grid model was created having vertically varying layers based on interpreted well logs. All the available petrophysics, PVT, pressure and production data were incorporated in the sector model along with completion and history details. The numerical model predictions are more realistic as compared to conventional methods. Comparison with actual performance of the well for ~1 year shows that it is closely in agreement with prediction from numerical model whereas results from analytical methods were showing pessimistic forecast with significant offset from actual well rates. The calibration of numerical model with the actual performance provided confidence on the model for field development. To develop and optimally produce the field, various sensitivities were run on the model which concluded drilling of a new horizontal well in the structure with multi-stage fracs to be the best option for improving production rate and significant recovery from the field. This study concludes that for tight oil reservoirs, conventional analytical methods such as material balance or DCA do not provide adequate results in comparison to numerical reservoir simulation technique. Even simple grid model is best suited because it incorporates reservoir heterogeneities, important structural complexities, overall acreage and can better predict dynamic behavior of these reservoirs. This case study is unique in comparison with the conventional sector modelling in a way that it captures the structural shape, different petrophysical properties for each layer based on well logs & offset core data, and also involves the history match of available data. It also effectively captures the impact of hydraulic fracturing in the well, hence helps to create realistic production profiles.