This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to a proposal of not more than 300 words; illustrations may not be copied. The proposal must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE,
Quantitative subsurface prediction and uncertainty analysis are critical success factors in all phases of exploration, development, and production projects, including regional evaluation, leasing, prospect maturation and drilling, appraisal and development planning, system selection, and reservoir management. Today, the E&P industry is moving into more frontier oil and gas provinces, such as deepwater and very deep plays, where well costs are very high. Consequently, the regional and in-field well densities are generally very low compared with historical exploration and development. Such sparseness of well control poses a challenge for quantitative subsurface analysis because it is difficult to establish spatial correlations of rock and reservoir properties with confidence, when the distances between wells are too large to assume geostatistical stationarity of earth model parameters.An effective approach to reduce subsurface uncertainty due to well sparseness is to build a 3D shared earth model using seismic data as "spatial glue." Under this approach, self-consistent 3D models are built for all interdependent earth model parameters that span a wide range of spatial scale and subsurface disciplines, and are constrained by physical laws and geologic scenarios. The self-consistency conditions of a 3D shared earth model provide tighter constraints and improved reservoir, rock and fluid property predictions, and more robust uncertainty analysis. This approach has been piloted in a number of play settings Figure 1. Pressure prediction workflow, incorporating all geophysical, geologic, and petrophysical data and analysis.Figure 2. Traverse through Mars-Ursa Basin (Mars-Princess-Ursa-Crosby) showing the original PSDM velocity model used as input to pressure prediction and an assortment of pressure diagnostic cubes that follow. (Color scales run from low to high values from bottom to top.)
The use of Distributed Acoustic Sensing for Strain Fronts (DAS-SF) is gaining popularity as one of the tools to help characterize the geometries of hydraulic fracs and to assess the far-field efficiencies of stimulation operations in Unconventional Reservoirs. These strain fronts are caused by deformation of the rock during hydraulic fracture stimulation (HFS) which produces a characteristic strain signature measurable by interrogating a glass fiber in wells instrumented with a fiber optic (FO) cable cemented behind casing. This DAS application was first developed by Shell and OptaSense from datasets acquired in the Groundbirch Montney in Canada. In this paper we show examples of DAS-SF in wells stimulated for a variety of completion systems: plug-and-perforating (PnP), open hole packer sleeves (OHPS), as well as, data from a well completed via both ball-activated cemented single point entry sleeves (Ba-cSPES) and coil-tubing activated cemented single point entry sleeves (CTa-cSPES). By measuring the strain fronts during stimulation from nearby offset wells, it was observed that most stimulated stages produced far-field strain gradient responses in the monitor well. When mapped in space, the strain responses were found to agree with and confirm the dominant planar fracture geometry proposed for the Montney, with hydraulic fractures propagating in a direction perpendicular to the minimum stress. However; several unexpected and inconsistent off-azimuth events were also observed during the offset well stimulations in which the strain fronts were detected at locations already stimulated by previous stages. Through further integration and the analysis of multiple data sources, it was discovered that these strain events corresponded with stage isolation defects in the stimulated well, leading to "re-stimulation" of prior fracs and inefficient resource development. The strain front monitoring in the Montney has provided greater confidence in the planar fracture geometry hypothesis for this formation. The high resolution frac geometry information provided by DAS-SF away from the wellbore in the far-field has also enabled us to improve stage offsetting and well azimuth strategies. In addition, identifying the re-stimulation and loss of resource access that occurs with poor stage isolation also shows opportunities for improvement in future completion programs. This in turn, should allow us to optimize operational decisions to more effectively access the intended resource volumes. These datasets show how monitoring high-resolution deformation via FO combined with the integration of other data can provide high confidence insights about stimulation efficiency, frac geometry and well construction defects not available via other means.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.