The Niobrara interval in the Denver-Julesberg (DJ) Basin contains several important unconventional hydrocarbon targets. However, the Niobrara is extensively faulted, which poses challenges for accurately landing and steering laterals in zone. Insight into small faulted structures in the Niobrara using traditional manual fault interpretation techniques is challenging because of the tuning thickness in seismic data. Fault throws less than the tuning thickness are difficult to interpret and incorporate into geosteering plans. Consequently, drillers frequently find themselves out of zone after crossing these small faults. Using independent information about fault locations and throws provided from multiple horizontal wells in the DJ Basin, this paper demonstrates the fault likelihood attribute (Hale, 2013) can resolve fault throws as small as 10 ft, allowing seismic-based well plans and unconventional project economics to be significantly improved.
Traditional geoscience data interpretation workflows in support of well planning can be tedious and time consuming, requiring manual fault picking on seismic profiles in conjunction with horizon tracing and gridding for structural mapping. The emergence of unconventional resource plays requires both more efficient geoscience workflows to support round-the-clock drilling operations and more detailed structural interpretations to help ensure laterals are steered along sweet spots. Pre-drill mapping of small-scale faults is therefore of particular importance for safe operations and helping ensure that lateral wells stay in zone.
Recent advances in fault-sensitive post-stack seismic attributes are changing the way subsurface professionals think about faults and how to map them in 3D space. In particular, the fault likelihood attribute (Hale, 2013) has provided a breakthrough improvement in the quality of seismic-derived fault attributes. Typically, the fault likelihood attribute is used in exploration settings to rapidly generate a broad-scale structural interpretation, being used both as a guide to manual fault interpretation and as input into automated fault extraction algorithms. This paper demonstrates the value of fault likelihood in development settings for assisting the well planning and geosteering process.
Gaither Draw Unit is a heterogeneous and tight formation with an average permeability less than 0.1 mD. After more than 1.7 MMSTB water injection, there was no clear indication or benefit of the injected water from any producer. However, knowing the distribution of the injected water is critical for future well planning and quantifying the efficiency of injection. The objective of this study is to show how the Capacitance-Resistance Model (CRM) was used on this field and validated using other independent methods.
The CRM model describes the connectivity and the degree of fluid storage quantitatively between injectors and producers from production and injection rates. Rooted in material balance, signals from injectors to producers can be captured in the CRM. Using constrained nonlinear multivariable optimization techniques, the connectivity is estimated in the selected portion of the field through signal analysis on injection and production rates. In this tight formation, the whole field is divided into seven regions with one injection well and surrounding producers to conduct CRM analysis. We further use integrated but independent approaches to validate the results from CRM. The validation includes full field modeling and history match and fluid level measurement using echometering technology.
This paper focuses on a real field water flooding project in Gaither Draw Units(GDU). CRM is used to detect reservoir heterogeneity through quantifying communication between injectors and producers, and attains a production match. The fitting results of connectivity through CRM indicate permeability regional heterogeneity, which is consistent with full field modelling. The history matched full field model presents the saturation distribution showing that the majority of injected water mainly saturates the surrounding regions of injectors, and the low transmissibility slows down the pressure dissipation. Overall, the comprehensive interpretation obtained through these three independent methods is consistent, and is very useful in planning infill well drilling and future development plan for the Gaither Draw Units.
This paper shows that it is critical to integrate different sources of data in reservoir management through a field case study. The experience and observations from this asset can be applied to other tight formations being developed with water flooding projects.
This case study reviews the full development cycle of Devon Energy's Parkman asset in the Powder River Basin, from exploitation to infill drilling. The focus is on successive adjustments to drilling and completion design, with the objective of net-present-value (NPV) optimization. This effort is supported primarily through timely collection and interpretation of data, which has awarded Devon with exceptional returns on investment with reasonably low risk and capital expenditure, even in a low oil price environment. Devon's success in the Parkman is largely due to the practical and adaptive nature of the underlying engineering workflows supporting key decisions as well as management's strong support for the value of information.
The methodology is based on an integrated workflow, combining core, open-hole logs, reservoir surveillance (radioactive tracers and fiber optics) along with production and flowing pressure data. Rate Transient Analysis, fracture modeling and reservoir simulation are central components of the integrated model. At every stage in the development cycle, the model is calibrated to the latest data, at which point sensitivities can be run to evaluate alternative scenarios for optimizations required for the next stage. Through successive drilling and completion improvements, Devon has successfully increased the NPV of their Parkman asset. The steps toward optimization include increasing lateral length from 4,000' to 9,500'; adjusting cluster spacing between 60' and 100' and transitioning their completion method from plug-andperf to pinpoint. Understanding the impact of oil price is critical for achieving these optimizations. The improvements were supported by reservoir simulation and ultimately confirmed with field data, which is included in this work.
This work brings to the forefront lessons that could add significant value to the business of any unconventional operator: A practical methodology for quickly converting collected data into actionable knowledge; allowing continual adjustments to be made to an unconventional completion design that maximize its performance and economic benefit Integration of engineering and geoscience disciplines to better understand the major influences on well performance in an unconventional play Optimizing completion design to maximize the profitability of a field development plan. In the case of the Parkman, pinpoint proves to be a more optimal completion technique
A practical methodology for quickly converting collected data into actionable knowledge; allowing continual adjustments to be made to an unconventional completion design that maximize its performance and economic benefit
Integration of engineering and geoscience disciplines to better understand the major influences on well performance in an unconventional play
Optimizing completion design to maximize the profitability of a field development plan. In the case of the Parkman, pinpoint proves to be a more optimal completion technique