No abstract

In the Rocky Mountain region of the US, nearly every well is hydraulic fracture stimulated to produce commercial volumes of oil and gas. The starting point for designing these treatments is an understanding of the in-situ stress profile. To calculate the in-situ stress profile, one must have an understanding of the mechanical rock properties and the pore pressure variations throughout the wellbore. Pore pressure can be measured in the permeable zones and in-situ stress can be calculated by the modeling of closure stress from pre-fracture pressure testing. But these tests are rarely performed in the nonreservoir rocks above and below the fracture stimulation treatment. The challenge for the stimulation design engineer is to determine the mechanical rock properties in and around the treatment interval. Calculating in-situ stress with the uniaxial strain equation requires the knowledge of Poisson's ratio, Young's modulus, pore pressure, and overburden pressure. Classically, the static Poisson's ratio (PR) and Young's modulus (YMS) are calculated by using the results from a dipole sonic log. However, in most fields, less than 1% of the wells requiring stimulation have dipole sonic data. In the absence of dipole sonic information, conventional wireline log data can be used to quite effectively calculate mechanical rock properties by using basic petrophysical relationships and artificial neural networks. The composite method to determine mechanical rock properties uses five methods to estimate the compressional slowness (DTC), seven methods to estimate shear slowness (DTS), eight methods to estimate PR, and and nine methods to determine YMS. The composite model PR and YMS are error minimized by a weighted averaging technique that honors the most reliable correlations. The composite modeled PR and YMS values are then used as inputs for a continuous calculation of the minimum horizontal stress (Shmin). The validation of the new composite model PR and YMS was confirmed in three ways. First, the composite model results are compared to the values determined in the lab from actual core samples. Second, the composite model results were compared to calculations using measured DTC and DTS in wells where dipole sonic tool measurements were recorded across a field in Wyoming. Finally, the composite model results were validated by using stimulation treatment pressure history matches on the Pinedale anticline in southwestern Wyoming. The goal of the composite model is to provide a robust rock property solution with or without sonic log data to eliminate the mechanical rock properties as a variable in fracture stimulation treatment design and history matching. Introduction In the world of hydraulic fracture stimulation modeling, the starting place is always the determination of the mechanical rock properties used to derive the minimum horizontal stress profile. Where does one get such data? In an ideal world, the easy answer is to calculate them by using the compression and shear slowness from a dipole sonic log. In the real world, however, dipole sonic logs are not a common tool included in the typical wireline logging suite. What does one do in the frequent case in which no sonic data is available? Most stimulation design models have some type of generic table of rock properties, based on a lithologic description or rock type. A stress profile built using this approach can easily lead to gross oversimplification of the stress profile throughout the wellbore. What about using multiple petrophysical relationships and a statistical error minimization approach to estimate the rock properties using conventional wireline log data?

Throughout the past decade, the number of horizontal wells drilled in the United States has continued to increase. Several fields currently being developed are only economical with the application of horizontal wells. One such area where this is the case is in the Bakken formation in the Williston Basin. In the Bakken, operators have used a variety of different completion methods ranging from multilateral, openhole wellbores to single lateral cemented liners. Regardless of the completion method, hydraulic fracturing is required for increased oil recovery. In the past two years, most operators have started to converge on running uncemented liners with external packers as the preferred completion method. The purpose of the external packers is to compartmentalize the wellbore for more efficient fracture stimulation, as demonstrated by Miller (2008). As this type of completion becomes more common, three key questions should be addressed:how does the fracture initiate in an open-annulus wellbore (transverse or longitudinal),what is the affect of the packer type on the stress on the wellbore surrounding the packer, andcan any of this be used to help with fracture design, or does it even matter? Finite-element modeling provides several advantages for modeling the different physical characteristics and responses for a system with very different sensitivities across the scale of the model. In the cases considered in this paper, the edge of the wellbore must be finely modeled to take into account the hoop stresses developed as a result of removing the rock volume from the original stress state. This is further complicated by the effects of orientation of the wellbore relative to the maximum and minimum horizontal stress and of different packers placed in the wellbore for isolation between stages. Conclusions on fracture-initiation behavior based on typical wellbore geometries, physical-rock properties, in-situ stress scenarios, and common packers are presented in this paper. Introduction The Bakken formation is present in only the subsurface of the Williston basin, which underlies much of North Dakota (ND), eastern Montana (MT), and northwestern South Dakota (SD), extending into Canada (Fig. 1). The Upper-Devonian Lower- Mississippian Bakken formation is composed of two organic-rich shale members that encase the clastic/carbonate Middle Member and overlay the Devonian Sanish/Three Forks formations (Fig. 2). Presently, there have been more than 650 horizontal wells drilled in MT, and nearly 600 wells drilled in ND. The majority of these wells were drilled in the Middle Member of the Bakken. Some wells are now being drilled in the Sanish/Three Forks, but the majority drilled continue to be in the Middle Member of the Bakken. According to a 2008 U.S. geological survey (USGS), there are an estimated 3.0 to 4.3 billion barrels of undiscovered, technically recoverable oil in the Bakken formation. This makes the Bakken formation the largest current USGS-oil assessment in the lower 48 states. It is also the largest "continuous" oil accumulation ever assessed by the USGS. Advances in drilling and completing long horizontal laterals have been the key to this large, technically recoverable oil volume. The Middle Member is a relatively thin section (10 to 30 ft) when compared to other formations being completed with horizontal wells, some hundreds of ft in height. Fracture gradients in the Middle Member range from 0.70 psi/ft in MT, to more than 0.85 psi/ft in areas of ND. The pore pressure also varies across the basin, ranging from 0.50 psi/ft in MT to 0.63 psi/ft in areas of ND. The maximum horizontal-stress orientation has been measured at 340° in MT, but varies across the basin, turning to the NE direction. There is little stress anisotropy between the maximum and minimum horizontal stress, which results in some complexity to the hydraulic-fracture geometry. This complexity has been noted by Bessler (2007), where interference has been observed in wells located E-W from the treatment well.

Experiments using acoustic sensors to monitor stress changes and hydraulic fracture propagation in moderate size, layered rock samples are described in this paper. The results show that microseismic event locations closely follow the actual growth of the hydraulic fracture, especially near the well bore. More events are detected and localized near the acoustic transducers indicating that signal attenuation is significant. In the work performed, event location based on automated picking techniques is not yet accurate enough to make diagnostic conclusions about fracture propagation near and through the different layered materials. Advanced processing techniques being developed in industry may well have the additional resolution necessary to focus some of these more subtle events at the laboratory scale. The conducted experiments indicated that controlling hydraulic fracture growth in laboratory-sized samples is difficult in small and moderate sized samples, and dynamically changing flap jack pressures, and injection rates and pressures is mandatory to slow the fracture growth for proper analysis once it has initiated. A key outcome of the work is the recognition that rocks emit substantial amounts of acoustic energy when stressed at incremental pressure levels of only a few psi, which corroborates a model of rocks as being meta-stable materials and explains frequently observed field phenomena. Further advancements in the use of acoustic monitoring at the laboratory scale are warranted and significant breakthroughs are possible for non-invasive investigations of solid and layered materials under stress.

Utilizing fluids that have friction reducing qualities and/or viscosity building properties are desirable for hydraulic fracturing in multi-stage horizontal wells. Horizontal wells targeting the Middle Bakken used various fluid types for stimulation were analyzed in this study. This paper uses data analytics to show that the answer to "which fluid type is better?". By applying statistical methods of k-means clustering, t-tests as well as multivariate analysis, a more robust answer was obtained in the comparison between slickwater, crosslinked gel, linear gel, hybrid systems, and Self-Suspending proppant. The first part of the analysis compared independent variables (lateral length, true vertical depth (TVD), liquid loading, sand loading, and fluid type) to well productivity in 532 Middle Bakken wells. Multivariate analysis was conducted to determine the dominant variables. Based on those, the data were filtered in two different ways: first on sand loading and secondly on lateral length and TVD. The second part of the analysis applied distinct k-means clustering. A multivariate model was built to accommodate the influence of each variable in each cluster. Having found inconclusive results, ANOVA (Analysis of Variance) analysis was conducted to analyze which fluid type resulted in the best overall well production. Several conclusions are demonstrated in this paper. First, industry stimulation treatment data are rarely crafted with thorough Design of Experiment rigor. This challenges many of the assumptions in statistics and data analytics to provide for unbiased analysis. Second, while lateral length, TVD and total sand are all seemingly independent variables physically, they were found to have a certain degree of statistical collinearity; a measure of variable dependence. Third, it was shown that wells using slickwater and 1Self-Suspending Proppant outperform wells using other proppant types in terms of overall production in the Middle Bakken. Hence, while the perfect frac job still eludes us, establishing a framework of unbiased analysis provide us with a robust approach to answer one of the key questions of our day: which fluid type is better? The novel thing about this type of analysis is that it takes thousands of different types of data points and narrows down what the most important variables in the data are. In an age where data is king, this approach follows statistic principles designed for handling such data.