Reservoir connectivity is often an important consideration for reservoir management. For example, connectivity is an important control on waterflood sweep efficiency and requires evaluation to optimize injection well rates. The uncertainty of sandbody distributions, however, can make interwell connectivity prediction extremely difficult. Percolation models are a useful tool to simulate sandbody connectivity behavior and can be used to estimate interwell connectivity. This study discusses the universal characteristics of different sandbody percolation models and develops an efficient percolation method to estimate interwell connectivity. Using King and others results for fluid travel time between locations in a percolation model, we developed a method to estimate interwell connectivity. Three parameters are needed to use this approach: the sandbody occupied probabilitysandp, the dimensionless reservoir length, and the well spacing. To evaluate this new percolation method, the procedure was coded using Visual Basic and Mathematica and the results compared to those from two other methods, a simple geometrical model and Monte Carlo simulation. All these methods were applied to estimate interwell connectivity for the D1, D2, and D3 intervals in the Monument Butte field. The results suggest that the new percolation method can give reasonable effective-square sandbody dimensions and can estimate the interwell connectivity accurately for thin intervals with sandp in the 60% to 80% range. The proposed method requires that the reservoir interval for evaluation be sufficiently thin so that 2D percolation results can be applied. To extend the method to 3D cases, we propose an approach that can be used to estimate interwell connectivity for reservoirs having multiple, noncommunicating layers, and that considers the weight of each interval for multilayer estimation. This approach is applied to the three-layer case of Monument Butte field and the estimates showed the method gives useful results for well pattern design. For example, water saturation and interval thickness affect the weight of each interval to be included in the multilayer estimation. For thick intervals or heterogeneous sandbody distributions, the percolation method developed here is not suitable because it assumes thin layers. Future percolation research will be needed to adapt this new percolation method.

Reservoir connectivity is often an important consideration for reservoir management. For example, connectivity is an important control on waterflood sweep efficiency and requires evaluation to optimize injection well rates. The uncertainty of sandbody distributions, however, can make interwell connectivity prediction extremely difficult. Percolation models are a useful tool to simulate sandbody connectivity behavior and can be used to estimate interwell connectivity. This study discusses the universal characteristics of different sandbody percolation models and develops an efficient percolation method to estimate interwell connectivity. Using King and others results for fluid travel time between locations in a percolation model, we developed a method to estimate interwell connectivity. Three parameters are needed to use this approach: the sandbody occupied probabilitysandp, the dimensionless reservoir length, and the well spacing. To evaluate this new percolation method, the procedure was coded using Visual Basic and Mathematica and the results compared to those from two other methods, a simple geometrical model and Monte Carlo simulation. All these methods were applied to estimate interwell connectivity for the D1, D2, and D3 intervals in the Monument Butte field. The results suggest that the new percolation method can give reasonable effective-square sandbody dimensions and can estimate the interwell connectivity accurately for thin intervals with sandp in the 60% to 80% range. The proposed method requires that the reservoir interval for evaluation be sufficiently thin so that 2D percolation results can be applied. To extend the method to 3D cases, we propose an approach that can be used to estimate interwell connectivity for reservoirs having multiple, noncommunicating layers, and that considers the weight of each interval for multilayer estimation. This approach is applied to the three-layer case of Monument Butte field and the estimates showed the method gives useful results for well pattern design. For example, water saturation and interval thickness affect the weight of each interval to be included in the multilayer estimation. For thick intervals or heterogeneous sandbody distributions, the percolation method developed here is not suitable because it assumes thin layers. Future percolation research will be needed to adapt this new percolation method.

This project estimates interwell connectivity, a characteristic that is crucial to determine reservoir continuity while developing a waterflooding project. It tests the combination of Fourier transforms (FT’s) of the flow rate data and analytical solutions from analog electrical circuits to infer the inverse diffusivity coefficient (IDC). I solved the transmission line equation analytically for 0D, 1D, and 2D resistance/capacitance (RC) network models and used those solutions to compare with the flow rate FT’s to determine the diffusivity parameters. I used the analogy between the electrical response of RC networks and the fluid response of permeable reservoirs on the basis of the similarities in the governing equations. I conclude that the analogy works accurately in simple reservoirs, where the assumptions of an analytical solution are met, i.e. single-phase fluid and a homogeneous system. For two-phase liquid cases, I determined that the analogy remains applicable because we still could produce accurate interwell connectivity information. When I investigated cases with dissolved-gas production around the wellbore, however, the analogy broke down and the results were not as good as the liquid systems.

Interwell connectivity is a very important piece of the puzzle for petroleum engineers. To optimize the injection well flow for increasing the production rate, interwell connectivity is a very important parameter. To build a model that works with better precision and with less effort has always been desired by reservoir engineers. In this study we developed an electric circuit network model (referred as the admittance or ymodel) for calculating the admittance parameters to predict branch flow rates (injectorproducer well pair) of oil reservoirs with precision. The y-model is very simple and efficient model that can predict branch flow very efficiently. Injection and production flow rates are the key data used in this model, which also happens to be the most abundant data for oil reservoirs. Injector well bottom-hole pressure data can also be used in this model if available. The governing equation of the electric circuit analogy of well to well flow rates in the oil reservoir is based on Ohm’s law for admittance. A mathematical procedure is also being developed for this circuit network model which solves a series of equations and finds unique solutions for the admittances and branch flows. These results can further be used for predicting the production flow rate for individual producer well. The model shows very good agreement with the exploration data of real oil reservoir. / text

Using injection and production data, we can evaluate the connectivity between injector and producer well pairs to characterize their interwell regions and provide a tool for waterflood management. The capacitance model (CM) has been suggested as a phenomenological method to analyze the injection and production data for these purposes. Early studies involving reservoir simulation have shown CM to be a valuable tool but also have revealed several shortcomings. Many of these deficiencies have become more transparent in analyzing field data. This work consists of two parts: in the first part, we investigate some of the shortcomings of the CM and attempt to overcome them by modifying the algorithms. In the second part, we relate the problem of interwell connectivity to the rigorous concept of Multiwell Productivity Index (MPI) and provide a semi analytical approach. We have developed two modifications on the CM: the segmented CM that can be used where bottomhole pressures (BHP) are unknown and may change during the analysis interval, and the compensated CM that overcomes the requirement to rerun the model after adding a new producer or shutting in an existing producer. If both BHP changes and shut-in periods occur, the segmented and compensated CMs can be used simultaneously to construct a single model for a period of data. We show several hypothetical cases and a field case where these modifications generate a more reliable evaluation of interwell connectivity and increase the R2 of the model up to 15%. On the other hand, the MPI-based approach can predict the reservoir performance analytically for homogeneous cases under specific conditions. In the heterogeneous cases, this approach provides a robust connectivity parameter, which solely represents the reservoir heterogeneity and possible anisotropy and hence allows improved information exchange with the geologist. In addition, this connectivity parameter is insensitive to possible variations of skin factor and changes in number of wells. A further advantage of the new method is the flexibility to incorporate additional information, such as injector BHP, into the analysis process. We applied this approach on several hypothetical cases and observed excellent evaluation of both reservoir performance and connectivity.

The demand for oil and gas is increasing yearly, whereas proven oil and gas reserves are being depleted. The potential of stripper oil and gas fields to supplement the national energy supply is large. In 2006, stripper wells accounted for 15% and 8% of US oil and gas production, respectively. With increasing energy demand and current high oil and gas prices, integrated reservoir studies, secondary and tertiary recovery methods, and infill drilling are becoming more common as operators strive to increase recovery from stripper oil and gas fields. The primary objective of this research was to support optimized production of oil and gas from stripper well fields by evaluating one stripper gas field and one stripper oil field. For the stripper gas field, I integrated geologic and engineering data to build a detailed reservoir characterization model of the Second White Specks (SSPK) reservoir in Garden Plains field, Alberta, Canada. The objectives of this model were to provide insights to controls on gas production and to validate a simulation-based method of infill drilling assessment. SSPK was subdivided into Units A ? D using well-log facies. Units A and B are the main producing units. Unit A has better reservoir quality and lateral continuity than Unit B. Gas production is related primarily to porosity-netthickness product and permeability and secondarily to structural position, minor structural features, and initial reservoir pressure. For the stripper oil field, I evaluated the Green River formation in the Wells Draw area of Monument Butte field, Utah, to determine interwell connectivity and to assess optimal recovery strategies. A 3D geostatistical model was built, and geological realizations were ranked using production history matching with streamline simulation. Interwell connectivity was demonstrated for only major sands and it increases as well spacing decreases. Overall connectivity is low for the 22 reservoir zones in the study area. A water-flood-only strategy provides more oil recovery than a primary-then-waterflood strategy over the life of the field. For new development areas, water flooding or converting producers to injectors should start within 6 months of initial production. Infill drilling may effectively produce unswept oil and double oil recovery. CO2 injection is much more efficient than N2 and CH4 injection. Water-alternating-CO2 injection is superior to continuous CO2 injection in oil recovery. The results of this study can be used to optimize production from Garden Plains and Monument Butte fields. Moreover, these results should be applicable to similar stripper gas and oil field fields. Together, the two studies demonstrate the utility of integrated reservoir studies (from geology to engineering) for improving oil and gas recovery.

Much effort of reservoir engineers is devoted to the time-consuming process of history matching in a simulator to understand the reservoir complexity. Its accuracy is debatable because only a few inputs are known. Several analytical tools have been developed to investigate reservoir heterogeneity. The reciprocal productivity index (RPI) is a tool to measure the pressure support observed at a producer. The log (water-oil ratio or WOR) plot can be used to indicate the presence of a channel. A capacitance-resistance model (CRM) is a simple tool to estimate the connectivity between a producer-injector pair from the production/injection and pressure data. Generally field operators implement an improved recovery plan such as water-alternating-gas (WAG) flood to improve displacement efficiency. However, the existence of heterogeneity compromises its performance. The first objective of this study is to improve the assessment of tertiary flood performance by integrating the CRM with other analytical tools. The integrated method was applied to a miscible flood field in West Texas. The results suggest strong interwell connectivity found more frequently in the NE-SW direction and the different preferential flow paths of injected CO2 and water. Overall, the results provide insights into the current flood status. The operating conditions of a producer dynamically change because of well/field constraints. These changes can induce significant interference in other wells, which cannot be captured by CRM. The second objective of this study is to develop a capacitance-resistance model with producer-producer interaction (CRMP-P). The CRMP-P, derived from the continuity and Darcy’s equations, accounts for producer-producer interactions. The CRMP-P was applied to data from three different reservoir models. The results suggest that the CRMP-P could fit the data with higher precision than CRM. Consequently, the CRMP-P estimates of reservoir properties are more accurate. Moreover, the estimated transmissibility between producers is in agreement with the reservoir models. The CRMP-P was also applied to Omani field data. The transmissibility results are consistent with previous study and the drilling information. The more accurate information on producer-producer interactions and reservoir properties can assist in history-matching, locating infill wells, and reservoir management planning. / text