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.

This paper presents a data driven approach to answer the question of whether premium, high strength white sand proppant, while more expensive than regional (brown) sand, is justified due to its alleged ability to make better producing wells. For this study, 739 horizontal wells with production, and stimulation data were used in a robust statistical approach to conclude that, for the most common set of well characteristics, white sand will produce a superior NPV weighted economic outcome than lower cost regional (brown) sand alternatives. While there are wells in this analysis that did not produce this robust conclusion of "white sand is better", none of them produced an outcome that "brown sand was better". Rather, several of the wells simply had results that were statistically inconclusive. This paper serves as a good example of what data are needed to perform such an analysis and the challenges of normalizing ‘first order effects’ that dominate the influence on well productivity (TVD, lateral length, and proppant intensity) while attempting to ascertain the influence of 'second order’ factors such as Sand Type. Becoming familiar and adept at these analysis methods should facilitate the statistical verification of other second order effects on finding the optimal stimulation treatment.

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Early identification of zones with possible good production (“sweet spots”) in shale plays is important to optimize the return on capital investment. Analyzing elastic rock properties helps in the identification of sweet spots in the Vaca Muerta Formation. The vertical and lateral heterogeneity of total organic carbon (TOC) and carbonate content are two critical factors that partly control the geomechanical response of the rock. Unconventional reservoirs have low permeability, making it necessary to define brittle zones to stimulate the rock and induce fractures to produce. This leads to the definition of mineralogy ranges that help discriminate brittle and ductile rock based on rock properties. This study is based on a 3D seismic survey and several vertical pilot wells with full log suites. Horizon interpretations and logs are used to build a low-frequency model that together with prestack seismic data is input for seismic inversion. Inversion results predict Poisson's ratio (v) directly, and TOC, carbonate content, density (ρ), and Young's modulus (E) are obtained through a neural network approach. The product (Eρ) is an alternative parameter to describe brittle rock when ρ cannot be obtained reliably. As elastic parameters, E and v are related to the geomechanical response of the rock, aiding in the discrimination of brittle and ductile rock. An inverse relationship between TOC and E is observed and quantified, but the relationship is very poor with v. Likewise, a direct relationship between carbonate content and E is identified but not with v. E plays the largest role in the identification of brittle rock within the Vaca Muerta Formation. Rock-physics relationships to describe sweet spots (zones with hydrocarbon presence and brittle rock) differ between shale plays around the world. The detailed 3D model from seismic inversion and clustering makes it possible to determine the best zones for placement of schematic horizontal wells.

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