Despite modern technological advancements in well drilling and completion, our understanding of hydraulic fracture geometry remains virtually the same as it was at least a decade ago. A critical approach to fracture treatment diagnostics involves an accurate evaluation of near-wellbore perforations efficiency and detection of hydraulic fractures away from the wellbore. The main limitation of currently available fracture diagnostic techniques is that they provide no information about the propped and conductive fractures beyond the wellbore. A cross-dipole acoustic tool and deep shear wave imaging (DSWI) processing are able to detect hydraulic fracture-induced changes within the vicinity and beyond the wellbore. In the near-wellbore region, the acoustic wave transit time increases substantially through the frac sand. The increase in transit time is a function of frac sand porosity. In the mid-field region beyond the wellbore (at approximately one hundred feet), changes in acoustic wave's reflection amplitude between pre- and post-frac measurements illustrate the induced (conductive) fractures and are a strong indicator of the presence of the fracture network away from the borehole. In addition, a three-dimensional fracture radius network model generated from DSWI data can provide greater insight, compared to seismic imaging methods for example, about the presence, location, and characteristics of natural and hydraulically induced fractures. The three-dimensional fracture network model created via DSWI can be more readily used in workflows or tools associated with reservoir modeling and fracture modeling. A novel hydraulic fracture diagnostic technology based on acoustic measurements enables efficient evaluation of the completion and quick, cost-effective hydraulic fracture mapping in the mid-field region. The ability to run a single tool before and after the hydraulic fracture treatment makes this tool a unique solution that helps customers make smart decisions to improve well economics.

Optimizing the drilling and completion phases of a typical oil or gas wellbore requires geomechanical analysis to characterize the in-situ stresses. Stress profiles and lateral tectonic stresses can be determined and calibrated through MicroFrac testing and by modeling borehole breakouts observed in borehole image logs.Several wells in the Campamento 1 field (the first field in the Neuquén Basin) were drilled with the tight basement formation in the Neuquén province in Argentina as the primary target. These wells were evaluated to estimate the stress profile using anisotropic mechanical properties and to identify suitable intervals to fracture. There are several tests and analyses that can typically be used to calibrate horizontal stresses. These tests and analyses include leak-off test (LOT), extended leak-off test (XLOT), minifrac test, step-rate-injection test (SRT), MicroFrac test, core strain measurement (Anelastic Strain Recovery, ASR), analysis of borehole breakouts and induced fractures from specific image logs.The objective of this paper is to show the use of borehole breakouts and hydraulically induced fracture initiation and closure pressures to calibrate the magnitude of the horizontal stresses in a vertical well. Breakouts are typically more visible in an acoustic image log and are expected to occur in formations with lower strength and/or lower internal friction coefficient but sometimes they occur in intervals with high strength and stiffness sustaining high horizontal tectonic stresses.Examples are shown in the Campamento 1 field wells with borehole breakouts and minifrac data used in constraining the magnitude of the lateral tectonic strains. The horizontal stress profiles obtained from these vertical wellbores provide valuable information for predicting hydraulic fracture geometry, propagation and containment for subsequent reservoir stimulation jobs. Two vertical wells where coupled calibration with minifrac data and breakout modeling were done are presented to show how this calibration is achieved and how it helps to constrain the magnitude of the horizontal stresses in a vertical well.The results show how observed borehole breakouts and fracture pressures can help in constraining the magnitude of the lateral tectonic strains. These are important parameters in estimating the horizontal stress profile in the target reservoir and the stress contrast over the sub-adjacent and supra-adjacent formations.

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Is there a proven methodology for designing optimum multi-stage completions in unconventional reservoirs? Where to place frac stages, perforation clusters, and how to avoid geo-hazards? This paper attempts to cover some of the fundamental aspects of stage placement and hydraulic fracturing designs by discussing lessons learned from several major US basins. A holistic data-driven approach to completion optimization is based on the analysis of the production potential, favorable rock mechanical properties, and geological features along the entire wellbore. Lateral reservoir characterization, including advanced cuttings and gas analysis, high-definition imaging technologies of logging while drilling (LWD), and wireline (WL) deep shear wave imaging (DSWI) is used to optimize completion and stimulation designs. The suggested methodology goes beyond the wellbore proximity and provides insight to practical application of advanced formation evaluation (FE) technologies. To address the challenges of lithological and horizontal stress heterogeneity of unconventional reservoirs and to optimize multi-stage completions, a combination of three primary categories of reservoir characteristics are considered: "producibility", "fracability", and geofeatures. Deep shear wave imaging technique identifies spatial natural fractures, faults, and other structural features up to 100 ft from the wellbore. This technology in combination with advanced cuttings and gas analysis helped identify and avoid faults, as well as target natural fractures for greater productivity and maximized economic return in several US basins. The analysis of field data suggests that geomechanical, petrophysical, and geochemistry data should be reviewed holistically. The post treatment analysis reveals a direct correlation of microseismic activity with the density of natural fractures obtained from DSWI processing. The strong correlation between surface treating pressure, breakdown pressure, and the fracture intensity was also observed. This information allows characterization of complex fracture networks, qualification of near-wellbore tortuosity, and estimation and modification of the hydraulic fracture design prior to the stimulation treatment. The intervals with lower horizontal stress and higher brittleness indexes are treated with lower pressure, and achieve the target pumping rates significantly faster than "ductile" stages. Case studies demonstrate up to 45% production increase compared to offset wells. As opposed to geometric stage placement, the suggested integrated completion strategy ensures targeted stimulation with the optimum reservoir contact at the maximum operational efficiency. Advanced formation evaluation techniques provide insight to mitigate risk and reduce uncertainty, therefore maximizing return on investment in multi-stage completions.

As laterals become longer with more stages, large datasets make evaluation of the information complicated and time consuming. Variations in the obtained data adds more uncertainty to the main industry challenges and questions, such as optimal well spacing, optimal completion (e.g., type of completion, stage and cluster spacing), optimal stimulation treatment (e.g., proppant type, mass and concentration, fluid type, volume and composition). This paper describes a novel automated framework for completion optimization and post-fracture analysis. For pre-fracture analysis, this framework utilizes an integrated dataset from drilling and wireline logs, and automatically places clusters based on heterogeneity along the lateral. This information is then coupled with post-fracture data to examine fracture treating information (breakdown pressure, instantaneous shut in pressure, etc.) and correlate it to rock properties from each fracture stage. The newly identified correlation is looped back into the framework as a guideline for future frac design. The strong correlation between subsurface rock properties and hydraulic fracture treatment parameters suggests that the previous captured heterogeneity reveals the fracture design efficiency, and the multivariable evaluation process can be accelerated by the machine learning platform. A statistical model was built on geomechanical and petrophysical properties used to design fracture treatments. Meanwhile, the platform automatically evaluates fracture treating signatures (surface treating pressure, pumping rate, ISIP, etc.) and links them with subsurface information for each stage. When the model is trained with a sufficient amount of data, it can be used as a real-time advisor that suggests a fracture treatment schedule (pump rate, sand concentration, fluid volume). The fracture geometry as well as the treatment efficiency can be optimized with the enhanced design. The model can eliminate or reduce the need for expensive subsurface characterization logging tools and provide quick recommendations for changes to the treatment. This paper introduces an integrated solution to optimize fracture treatment design assisted by data analytics that can be further improved to solve other multivariable problems in the oil and gas industry.