Quantifying the Reliability and Value of 3D Land Seismic
- J. Eric Bickel (Texas A&M University) | Richard L. Gibson (Texas A&M University) | Duane A. McVay (Texas A&M University) | Stephen Pickering (WesternGeco) | John R. Waggoner (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- October 2008
- Document Type
- Journal Paper
- 832 - 841
- 2008. Society of Petroleum Engineers
- 5.1.1 Exploration, Development, Structural Geology, 1.6 Drilling Operations, 5.1.8 Seismic Modelling, 5.1.7 Seismic Processing and Interpretation, 5.1.9 Four-Dimensional and Four-Component Seismic, 5.1.5 Geologic Modeling, 5.1 Reservoir Characterisation, 5.8.7 Carbonate Reservoir, 5.5.2 Core Analysis, 1.2.3 Rock properties, 7.2.3 Decision-making Processes, 1.6.1 Drilling Operation Management, 5.5.8 History Matching
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Seismic data provide essential information for guiding reservoir development. Improvements in data quality hold the promise of improving performance even further, provided that the value of these data exceeds their cost. Previous work has demonstrated value-of-information (VOI) methods to quantify the value of seismic data. In these examples, seismic accuracy was obtained by means of expert assessment instead of being based on geophysical quantities. In addition, the modeled seismic information was not representative of any quantity that would be observed in a seismic image.
Here we apply a more general VOI model that includes multiple targets, budgetary constraints, and quantitative models relating poststack seismic amplitudes and amplitude-variation-with-offset (AVO) parameters to the quantities of interest for reservoir characterization, such as porosity and reservoir thickness. Also, by including estimated changes in data accuracy based on signal-to-noise ratio, the decision model can provide objective estimates of the reliability of measurements derived from the seismic data. We demonstrate this methodology within the context of a west Texas 3D land survey. This example demonstrates that seismic information can improve reservoir economics and that improvements in seismic technology can create additional value.
Reservoir characterization makes heavy use of seismic data both for selecting a target for drilling and, with time-lapse data, for monitoring the fluid movements in the reservoir to optimize production of hydrocarbons. Reservoir characterization requires good-quality seismic data for optimal results. Improvements in aspects of seismic acquisition, such as signal-to-noise ratio, bandwidth, receiver positioning, or maximum offset, may help improve images or AVO analyses, thereby increasing the level of knowledge about reservoir structure or properties.
However, modifications to acquisition procedures to estimate rock properties better or to improve subsalt images, for example, may increase expense of data acquisition and possibly experiment duration. The improved data quality must always be weighed against the additional cost.
Previous work has addressed valuing seismic data using the decision-analysis concept of VOI, including Stibolt and Lehman (1993), Waggoner (2000b, 2002), Begg et al. (2002), Pickering and Bickel (2006), and Bickel et al. (2006). Ballin et al. (2005) and Steagall et al. (2005) provide examples of actual seismic projects where VOI analyses shaped management decisions significantly. See Bratvold et al. (2007) for a review of VOI papers in the SPE literature.
One challenge of implementing VOI methodologies to value seismic data is the assessment of seismic accuracy. The studies discussed in the preceding paragraph rely on expert assessment and model seismic information at a high level. In many cases, these assessments are not tied directly to observable seismic signals. For example, some studies assess the probability that the seismic survey will report "success," "unswept," or "large reservoir," even though the actual signal from a seismic survey may be an amplitude reading. This gap between what seismic surveys actually report and what is needed in decision making makes the implementation of VOI techniques problematic (Bratvold et al. 2007). To address these concerns, several authors have performed historical look-backs to document the impact of seismic information [e.g. see Aylor (1999) and Waggoner (2000a)]. Another difficulty is appropriately modeling the decision-making environment and the role seismic information plays. Many authors implicitly embed downstream decisions in the seismic-accuracy assessment by assuming the chance of geologic success can only go up after commissioning a seismic survey (Head 1999; Waggoner 2000b, 2002). This mixing of probability assessments and decision making makes it difficult to understand the value of seismic in a specific situation.
Houck (2004) addressed some of these concerns by valuing seismic's ability to inform estimates of porosity in the context of a multiwell drilling program and tying the accuracy of seismic data to directly observable seismic signals. This paper also extends previous VOI studies by considering multiple targets and budgetary constraints. We extend Houck's results by investigating the accuracy and value of AVO and peak amplitude. Furthermore, we examine the ability of seismic information to inform estimates of multiple reservoir properties simultaneously (e.g., porosity, thickness, and water saturation). The resulting models allow quantification of the accuracy of information provided by seismic data and quantification of the information's economic value.
The contributions of this paper are three-fold. First, we illustrate a VOI method that directly relates observable seismic signals to reservoir properties and reservoir-management decisions. Second, we develop a seismic model that allows us to quantify objectively the accuracy of seismic information across a range of acquisition and processing techniques. Third, we quantify both the absolute value of seismic information and the relative value of improved seismic information within the context of a 3D land example situated in a hypothetical carbonate reservoir modeled after the McElroy field in west Texas.
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