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Abstract In some basins, large scale development of unconventional stacked-target plays requires early election of well targeting and spacing. Changes to the initial well construction framework can take years to implement due to lead times for land, permitting, and corporate planning. Over time, as operators wish to fine tune their development plans, completion design flexibility represents a powerful force for optimization. Hydraulic fracturing treatment plans may be adjusted and customized close to the time of investment. With a practical approach that takes advantage of physics-based modeling and data analysis, we demonstrate how to create a high-confidence, integrated well spacing and completion design strategy for both frontier and mature field development. The Dynamic Stimulated Reservoir Volume (DSRV) workflow forms the backbone of the physics-based approach, constraining simulations against treatment, flow-back, production, and pressure-buildup (PBU) data. Depending on the amount of input data available and mechanisms investigated, one can invoke various levels of rigor in coupling geomechanics and fluid flow – ranging from proxies to full iterative coupling. To answer spacing and completions questions in the Denver Basin, also known as the Denver-Julesburg (DJ) Basin, we extend this modeling workflow to multi-well, multi-target, and multi-variate space. With proper calibration, we are able generate production performance predictions across the field for a range of subsurface, well spacing, and completion scenarios. Results allow us to co-optimize well spacing and completion size for this multi-layer column. Insights about the impacts of geology and reservoir conditions highlight the potential for design customization across the play. Results are further validated against actual data using an elegant multi-well surveillance technique that better illuminates design space. Several elements of subsurface characterization potentially impact the interactions among design variables. In particular, reservoir fluid property variations create important effects during injection and production. Also, both data analysis and modeling support a key relationship involving well spacing and the efficient creation of stimulated reservoir volumes. This relationship provides a lever that can be utilized to improve value based on corporate needs and commodity price. We introduce these observations to be further tested in the field and models.
Abstract In unconventional resource plays, constructing a sound geological model that ties various well information is imperative for properly extracting and integrating well and seismic information and for predictive and prescriptive analytic workflows. Unlike conventional plays, unconventional plays that span basins have potentially tens of thousands of wells. Constructing geological models to include all wells and then updating them as additional ones become available can be a daunting task. When constructing large cross sections, regional stratigraphic patterns are easily discernible visually. Converting these geologic events and spatial patterns to digital information using the power of the computer and new machine learning techniques is becoming more important than ever as geoscientists attempt to "keep up" with all this information. This paper will cover a modern technology toward that end. Introduction Previous attempts have been made to pick geologic well tops automatically using expert systems (Olea et al.), neural networks (Luthi et al.), and dynamic programming (Lineman et al., Inazaki, Zoraster et al., Fang et al.). While these previous efforts have been helpful in defining the problems and building blocks to solve well-log correlation automatically, they have clearly been much less successful than has been observed in seismic picking algorithms that started in the 1980's. This is mainly owing to the nature of seismic data. Seismic traces are band-limited, closely spaced (on the order of meters) with neighboring traces almost identical to each other, and are consistent with the same start and ending times, sample rates, and vertical representation. These traits make correlating neighboring peaks, troughs and zero-crossings reasonably easy as compared to well logs, which are more widely spaced (on the order of hundreds to thousands of meters), have inconsistent depth ranges with possible gaps, and may be from highly non-vertical well bores. As more oil companies transition from exploration to resource recovery optimization and the number of new wells in well-known basins dramatically increases, geologic cross sections across these basins begin to take on more of a seismic look, as shown in Figures 1 and 2 below. When logs are hung on stratigraphic datums, as Figure 2 shows, geologic intervals are readily evident across many tens, if not hundreds or thousands of wells. Not only is the lateral consistency of strong events evident, such as the Codell in this case, but patterns of finer detail in the sequence stratigraphy (flooding surfaces, onlap, thickening and thinning from changing accommodation and sediment supply) become more visually apparent. Further refined picking of associated events is warranted but could prove tedious and time consuming if done manually.