|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
The Niobrara Shale in the United States has ramped up into a hot play that could soon bring an explosion of horizontal drilling in Colorado and Wyoming. The combination of horizontal drilling and multistage hydraulic fracturing is transforming the Niobrara from a target that has been drilled vertically and primarily for gas for nearly 100 years into a liquids-rich play that is capturing considerable attention. Speaking at the 2011 SPE Annual Technical Conference and Exhibition in Denver, John Ford, general manager of Colorado’s Wattenberg field at Anadarko, described the growing Niobrara activity as “really the next big thing.”
That optimism was understandable. In November, Anadarko announced that its leases at Wattenberg may hold more than a billion barrels of recoverable oil and natural gas. The statement noted company drilling success in 11 recent wells at the field, including the Dolph 27-1HZ horizontal well that showed initial production of more than 1,100 B/D of oil and 2.4 MMcf/D of natural gas. These latest wells have given the company confidence that it can drill between 1,200 and 2,700 wells in northeast Colorado, with approximately 160 wells planned for this year. Based on results so far, the company expects ultimate recovery of between 500 million and 1.5 billion bbl of oil, natural gas liquids, and natural gas on an equivalent basis.
Anadarko is not alone. Chesapeake Energy, Noble, Encana, and EOG Resources are among the largest acreage holders and the most active drillers of many companies—including numerous small independents—probing the Niobrara. Majors such as Shell and Marathon Oil have significant acreage.
There are more than 50 operators in or near the Wattenberg field alone. Situated north/northeast of the Denver area, Wattenberg is the largest producing field in the Denver-Julesburg (D-J) Basin and one of the largest onshore oil and gas fields in the US.
Reservoir Rock and Producing Regions
Although the Niobrara is usually referred to as a shale, its reservoir rock consists primarily of limestone or chalk intervals, said Steve Sonnenberg, professor of petroleum geology at Colorado School of Mines in a recent edition of the AAPG Explorer (published by the American Association of Petroleum Geologists). “The formation demonstrates facies changes that range from limestone and chalk in the eastern end to calcareous shale in the middle and eventually transitioning to sandstone farther west,” said Sonnenberg, a past president of AAPG. “Depth and thickness are highly variable.”
The Upper Cretaceous Niobrara Chalk in the Sand Wash Basin is characterized by having more terrigenous components than the Niobrara Chalk further to the east. This difference in lithology affects reservoir quality and the potential of the chalk as a matrix-producing reservoir. The degraded reservoir does not appear productive as a shaleoil reservoir, but may be productive as a shale-gas reservoir in the deeper and hotter parts of the Sand Wash Basin. The major objective of this paper is to present a preliminary characterization of the Niobrara Chalk as a shale-gas system in the northwest Sand Wash Basin.
Abstract Seismic attributes can be both powerful and challenging to incorporate into interpretation and analysis. Recent developments with machine learning have added new capabilities to multi-attribute seismic analysis. In 2018, Geophysical Insights conducted a proof of concept on 100 square miles of multi-client 3D data jointly owned by Geophysical Pursuit, Inc. (GPI) and Fairfield Geotechnologies (FFG) in the Denver-Julesburg Basin (DJ). The purpose of the study was to evaluate the effectiveness of a machine learning workflow to improve resolution within the reservoir intervals of the Niobrara and Codell formations, the primary targets for development in this portion of the basin. The seismic data are from Phase 5 of the GPI/Fairfield Niobrara program in northern Colorado. A preliminary workflow which included synthetics, horizon picking and correlation of 28 wells was completed. The seismic volume was re-sampled from 2 ms to 1 ms. Detailed well time-depth charts were created for the Top Niobrara, Niobrara A, B and C benches, Fort Hays and Codell intervals. The interpretations, along with the seismic volume, were loaded into the Paradise® machine learning application, and two suites of attributes were generated, instantaneous and geometric. The first step in the machine learning workflow is Principal Component Analysis (PCA). PCA is a method of identifying attributes that have the greatest contribution to the data and that quantifies the relative contribution of each. PCA aids in the selection of which attributes are appropriate to use in a Self-Organizing Map (SOM). In this case, 15 instantaneous attribute volumes, plus the parent amplitude volume, were used in the PCA and eight were selected to use in SOMs. The SOM is a neural network-based machine learning process that is applied to multiple attribute volumes simultaneously. The SOM produces a non-linear classification of the data in a designated time or depth window. For this study, a 60-ms interval that encompasses the Niobrara and Codell formations was evaluated using several SOM topologies. One of the main drilling targets, the B chalk, is approximately 30 feet thick; making horizontal well planning and execution a challenge for operators. An 8 X 8 SOM applied to 1 ms seismic data improves the stratigraphic resolution of the B bench. The neuron classification also images small but significant structural variations within the chalk bench. These variations correlate visually with the geometric curvature attributes. This improved resolution allows for precise well planning for horizontals within the bench. The 25 foot thick C bench and the 17 to 25 foot thick Codell are also seismically resolved via SOM analysis. Petrophysical analyses from wireline logs run in seven wells within the survey by Digital Formation; together with additional results from SOMs show the capability to differentiate a high TOC upper unit within the A marl which presents an additional exploration target. Utilizing 2d color maps and geobodies extracted from the SOMs combined with petrophysical results allows calculation of reserves for the individual reservoir units as well as the recently identified high TOC target within the A marl. The results show that a multi-attribute machine learning workflow improves the seismic resolution within the Niobrara reservoirs of the DJ Basin and results can be utilized in both exploration and development.
The Upper Cretaceous Codell and Niobrara formations are primary targets in the Wattenberg field (Figure 1) of the Denver- Julesberg basin in northwest Colorado. Although initial discovery of producible hydrocarbons in the Codell occurred in 1955, it was not until the early 1980s when larger scale completion began for the Codell and Niobrara wells throughout the field. Since the early 1980s, there have been more than 10,000 Codell and Niobrara completions in the Wattenberg field.
Originally, the Codell and Niobrara formations were completed individually within the same well. In the early 1990s, operators introduced a limited-entry style stimulation technique in an effort to effectively stimulate both formations with one hydraulic fracturing treatment. Although there is currently significant activity in the field with regard to horizontal well completion in both the Codell and Niobrara formations, extensive vertical well completion still exists. Various completion strategies are currently employed for vertical well completion, including individual stimulation of the Codell and Niobrara formations, limited-entry completion of both formations, and completion of only one of the producing horizons, leaving the other behind pipe. Furthermore, there are many cases of recompletions where an operator has re-entered a Codell-only well at a later date to complete the Niobrara.
Spatial sampling was employed to assess the effectiveness of various completion strategies in one area of the Wattenberg field. Spatial sampling is a documented method for comparing large groups of wells with their direct offsets. The original intent of the spatial sampling method was to identify underperforming wells; however, the method has also been employed as a way to compare various completion or stimulation techniques.
The independent holds around 400,000 net acres in the DJ Basin and hopes to increase production to more than 400,000 BOE/D by 2021. But the existing out here is dedicated for midstream. Relatively few Everybody kind of knows who has what, Denver-Julesburg (DJ) companies have large positions in the and we do development around that." Basin and overlapping Niobrara Shale. Denver also serves surge, attention has been drawn to the nearby competitors. Operators have flocked to West of SRC Energy, previously known as Synergy like in the Bakken and Permian where Texas, southeastern New Mexico, and Resources and one of the region's labor costs and turnover can be high. We don't have a lot of disposal leaner era for the industry. The expansive good rate of return" due mainly to low issues," which are common in areas such Permian alone, which covers more than well and takeaway costs, which makes it as the Permian. "The key is the big players--Anadarko, bulk of US oil production increases and He previously served as completions Noble, and PDC--feel the economics mergers and acquisitions over the last manager for Anadarko Petroleum's DJ compete with the best shale economics couple of years.