Quantifying infill potential in marginal oil fields often involves several challenges. These include highly heterogeneous reservoir quality both horizontally and vertically, incomplete reservoir databases, considerably large amounts of data involving numerous wells, and different production and completion practices. The most accurate way to estimate infill potential is to conduct a detailed integrated reservoir study, which is often time-consuming and expensive for operators of marginal oil fields. Hence, there is a need for less-demanding methods that characterize and predict heterogeneity and production variability. As an alternative approach, various authors have used empirical or statistical analyses to model variable well performance. Many of the methods are based solely on the analysis of well location, production and time data. My objective is to develop an enhanced method for rapid assessment of infill-drilling potential that would combine increased accuracy of simulation-based methods with times and costs associated with statistical methods. My proposed solution is to use reservoir simulation combined with automatic history matching to regress production data to determine the permeability distribution. Instead of matching on individual cell values of reservoir properties, I match on constant values of permeability within regions around each well. I then use the permeability distribution and an array of automated simulation predictions to determine infill drilling potential throughout the reservoir. Infill predictions on a single-phase synthetic case showed greater accuracy than results from statistical techniques. The methodology successfully identified infill well locations on a synthetic case derived from Cut Bank field, a water-flooded oil reservoir. Analysis of the actual production and injection data from Cut Bank field was unsuccessful, mainly because of an incomplete production database and limitations in the commercial regression software I used. In addition to providing more accurate results than previous empirical and statistical methods, the proposed method can also incorporate other types of data, such as geological data and fluid properties. The method can be applied in multiphase fluid situations and, since it is simulation based, it provides a platform for easy transition to more detailed analysis. Thus, the method can serve as a valuable reservoir management tool for operators of stripper oil fields.

Acquisition and interpretation of 3-D seismic data led DeAngelo and Hardage (2001) to describe the channel system in the south central Cut Bank area in Glacier County, Montana. The presence of a low velocity layer called Bentonite was also discovered in the area with the help of well-logs. Bentonite is a volcanic ash, which lies on both sides of the channel system and is absent within the channel. DeAngelo and Hardage (2001) shot a vertical seismic profiling (VSP) survey at well # 54-8 to analyze the formation structure in depth, since seismic signals around the reservoir area were unclear in the 3-D survey. This research attempts to estimate the attenuation properties of the Bentonite layer in the Cut Bank oil field. VSP data is processed for velocity information and estimation of seismic Q using the spectral ratios method (SRM). The SRM theoretically assumes that the propagating signal is a plane seismic wave traveling vertically from one point to another in a homogeneous model. The amplitudes at the start and end points are known and relate to each other with the attenuation coefficient in a frequency range. The relation between the seismic amplitudes at z distance from each other can be expressed as a linear function of frequency after a few modifications. SRM uses the linearity of the logarithmic ratio of the seismic amplitudes over a frequency range. In theory, ratios plotted against a frequency range must produce a flat line. However, in practice, the logarithmic ratios are expected to draw an approximate line (curve), where some of the data points deviate from the origin of the line. Thus fitting a line to the ratios curve and calculating the slope of this curve are necessary. Slope of the curve relates to the seismic attenuation coefficient and further to the seismic Q. The SRM results suggest that Bentonite may have a Q value as low as 5. This highly attenuative and thin (20 to 40 feet throughout the south central Cut Bank Unit) layer alters seismic signals propagating through it. A thorough analysis of the amplitude spectra suggests that seismic signals dramatically lose their energy when they pass through Bentonite. Low energy content of the signals below the Bentonite layer highlights that the recovery of the seismic energy is less likely despite the presence of multiples, which are known to affect the seismic signals constructively. Therefore, separation of reflected events is a greater challenge for the thin reservoir sand units lying underneath the Bentonite layer. Thus the Bentonite layer in the Cut Bank oil field has to be taken seriously and data processing should be done accordingly for better accuracy.

In South Central Cut Bank Sand Unit (SCCBSU) of Cut Bank field, primary production and waterflood projects have resulted in recovery of only 29 % of the original oil in place from heterogeneous, fluvial sandstone deposits. Using highresolution sequence stratigraphy and geostatistical analysis, I developed a geologic model that may improve the ultimate recovery of oil from this field. In this study, I assessed sequence stratigraphic concepts for continental settings and extended the techniques to analyze low-accommodation alluvial systems of the Cut Bank and Sunburst members of the lower Kootenai formation (Cretaceous) in Cut Bank field. Identification and delineation of five sequences and their bounding surfaces led to a better understanding of the reservoir distribution and variability. Recognition of stacking patterns allowed for the prediction of reservoir rock quality. Within each systems tract, the best quality reservoir rocks are strongly concentrated in the lowstand systems tract. Erosional events associated with falling baselevel resulted in stacked, communicated (multistory) reservoirs. The lowermost Cut Bank sandstone has the highest reservoir quality and is a braided stream parasequence. Average net-to-gross ratio value (0.6) is greater than in other reservoir intervals. Little additional stratigraphically untapped oil is expected in the lowermost Cut Bank sandstone. Over most of the SCCBSU, the Sunburst and the upper Cut Bank strata are valley-fill complexes with interfluves that may laterally compartmentalize reservoir sands. Basal Sunburst sand (Sunburst 1, average net-to-gross ratio ~0.3) has better reservoir quality than other Sunburst or upper Cut Bank sands, but its reservoir quality is significantly less than that of lower Cut Bank sand. Geostatistical analysis provided equiprobable representations of the heterogeneity of reservoirs. Simulated reservoir geometries resulted in an improved description of reservoir distribution and connectivity, as well as occurrences of flow barriers. The models resulting from this study can be used to improve reservoir management and well placement and to predict reservoir performance in Cut Bank field. The technical approaches and tools from this study can be used to improve descriptions of other oil and gas reservoirs in similar depositional systems.