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
- 1 in the last 30 days
- 641 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
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.
|File Size||1 MB||Number of Pages||10|
Ait-Messaoud, M., Boulegroun, M.-Z., Gribi, A. et al. 2005. New dimensionsin land seismic technology. Oilfield Review 17 (3): 42-53.
Aki, K. and Richards, P.G. 2002. Quantitative Seismology, secondedition. Sausalito, California: University Science Books.
Avasthl, J.M., Nolen-Hoeksema, R.C., and El Rabaa, A.W.M. 1991. In-Situ Stress Evaluation in theMcElroy Field, West Texas. SPEFE 6 (3): 301-309;Trans., AIME, 291. SPE-20105-PA. DOI: 10.2118/20105-PA.
Avseth, P., Mukerji, T., Mavko, G., and Veggeland, T. 1998. Statistical discrimination oflithofacies from pre-stack seismic data constrained by well log rock physics:Application to a North Sea turbidite system. SEG Expanded Abstracts17: 890-893. DOI:10.1190/1.1820632.
Aylor, W.K. Jr. 1999. Measuringthe Impact of 3D Seismic on Business Performance. JPT 51(6): 52-56. SPE-56851-MS. DOI: 10.2118/56851-PA
Bachrach, R. and Dutta, N. 2004. Joint estimation of porosity andsaturation and of effective stress and saturation for 3D and 4D seismicreservoir characterization using stochastic rock physics modeling and Bayesianinversion. SEG Expanded Abstracts 23: 1515-1518.DOI:10.1190/1.1845133.
Ballin, P.R., Ward, G.S., Whorlow, C.V., and Kahn, T. 2005. Value of Information for a 4D-SeismicAcquisition Project. Paper SPE 94918 presented at the SPE Latin Americanand Caribbean Petroleum Engineering Conference, Rio de Janeiro, 20-23 June.DOI: 10.2118/94918-MS.
Bär, W. and Dittrich, F. 1971. Useful formula for momentcomputation of normal random variables with nonzero means. IEEETransactions on Automatic Control 16 (3): 263-265.DOI:10.1109/TAC.1971.1099712.
Begg, S., Bratvold, R., and Campbell, J. 2002. The Value of Flexibility in ManagingUncertainty in Oil and Gas Investments. Paper SPE 77586 presented at theSPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 29September-2 October. DOI: 10.2118/77586-MS.
Bernardo, J.M. and Smith, A.F.M. 2000. Bayesian Theory. West Sussex,England: Wiley Series in Probability and Statistics, John Wiley & SonsLtd.
Bickel, J.E. 2006. Somedeterminants of corporate risk aversion. Decision Analysis 3(4): 233-251. DOI: 10.1287/deca.1060.0080.
Bickel, J.E., Gibson, R.L., McVay, D.A., Pickering, S., and Waggoner, J.2006. Value of seismic information with multiple drilling targets. Paper B012presented at the EAGE Conference and Exhibition, Vienna, Austria, 12-15June.
Bratvold, R.B., Bickel, J.E., and Lohne, H.P. 2007. Value of Information in the Oil andGas Industry: Past, Present, and Future. Paper SPE 110378 presented at theSPE Annual Technical Conference and Exhibition, Anaheim, California, USA, 11-14November. DOI: 10.2118/110378-MS.
Brealey, R.A. and Myers, S.C. 1991. Principles of Corporate Finance,fourth edition. New York City: McGraw-Hill.
Castagna, J.P. 1993. AVO analysis--Tutorial and review. InOffset-Dependent Reflectivity--Theory and Practice of AVO Analysis, No.8, ed. P. Castagna and M.M. Backus, Chap. 1, 3-36. Tulsa: Investigations inGeophysics series, Society of Exploration Geophysicists.
Clemen, R.T. and Reilly, T. 2001. Making Hard Decisions, secondedition. Pacific Grove, California: Duxbury Press.
Doyen, P.M. 1988. Porosityfrom seismic data: A geostatistical approach. Geophysics 53(10): 1263-1275. DOI:10.1190/1.1442404.
Eberhart-Phillips, D., Han, D-H., and Zoback, M.D. 1989. Empirical relationships amongseismic velocity, effective pressure, porosity, and clay content insandstone. Geophysics 54 (1): 82-89.DOI:10.1190/1.1442580.
Gallop, J. 2006. Faciesprobability from mixture distributions with non-stationary impedanceerrors. SEG Expanded Abstracts 25: 1801-1805.DOI:10.1190/1.2369874.
Gassmann, F. 1951. Uber die elastizität poröser medien.Vierteljahrschrift der Naturforschenden Gesellschaft in Zürich96: 1-23.
Gibson, R.L. Jr. 2005. Influence of internal reservoirstructure on composite reflection coefficients. SEG ExpandedAbstracts 24: 312-315. DOI:10.1190/1.2144330.
Head, K.J. 1999. How Could youPossibly Predict the Value of 3-D Seismic Before You Shoot It? Paper SPE56446 presented at the SPE Annual Technical Conference and Exhibition, Houston,3-6 October. DOI: 10.2118/56446-MS.
Houck, R.T. 2004. Predicting the economic impact of acquisition artifactsand noise. The Leading Edge 23 (10): 1024-1031.
Kellerer, H., Pferschy, U., and Pisinger, D. 2004. Knapsack Problems.Heidelberg, Germany: Springer-Verlag.
Kirkwood, C.W. 2004. Approximating risk aversion indecision analysis applications. Decision Analysis 1 (1):51-67. DOI:10.1287/deca.1030.0007.
Martello, S. and Toth, P. 1990. Knapsack Problems: Algorithms andComputer Implementations. West Sussex, UK: Wiley Interscience Series inDiscrete Mathematics and Optimization, John Wiley & Sons Ltd.
Mavko, G. and Mukerji, T. 1998. A rock physics strategy forquantifying uncertainty in common hydrocarbon indicators. Geophysics63 (6): 1997-2008. DOI:10.1190/1.1444493.
Mukerji, T., Avseth, P., Mavko, G., Takahashi, I., and González, E.F. 2001.Statistical rock physics: Combining rock physics, information theory, andgeostatistics to reduce uncertainty in seismic reservoir characterization.The Leading Edge 20 (3): 313-319.
Nolen-Hoeksema, R.C., Wang, Z., Harris, J.M., and Langan, R.T. 1995. High-resolution crosswell imaging ofa west Texas carbonate reservoir: Part 5--Core analysis. Geophysics60 (3): 712-726. DOI:10.1190/1.1443810.
Pickering, S. and Bickel, J.E. 2006. The value of seismic information.Oil & Gas Financial Journal 3 (5): 26-33.
Sato, H. and Fehler, M.C. 1998. Seismic Wave Propagation and Scatteringin the Heterogeneous Earth. New York City: Model Acoustics and SignalProcessing series, Springer-Verlag.
Shuey, R.T. 1985. Asimplification of the Zoeppritz equations. Geophysics 50 (4):609-614. DOI:10.1190/1.1441936.
Smith, T.M., Sondergeld, C.H., and Rai, C.S. 2003. Gassmann fluid substitutions: Atutorial. Geophysics 68 (2): 430-440.DOI:10.1190/1.1567211.
Spikes, K., Mukerji, T., Dvorkin, J., and Mavko, G. 2007. Probabilistic seismic inversionbased on rock-physics models. Geophysics 72 (5): R87-R97.DOI:10.1190/1.2760162.
Steagall, D.S., Gomes, J.A.T., Oliveira, R.M. et al. 2005. How to Estimate the Value ofInformation (VOI) of a 4D Seismic Survey in One Offshore Giant Field. PaperSPE 95876 presented at the SPE Annual Technical Conference and Exhibition,Dallas, 9-12 October. DOI: 10.2118/95876-MS.
Stibolt, R.D. and Lehman, J. 1993. The Value of a Seismic Option.Paper SPE 25821 presented at the SPE Hydrocarbon Economics and EvaluationSymposium, Dallas, 29-30 March. DOI: 10.2118/25821-MS.
Takahashi, I., Mukerji, T., and Mavko, G. 1999a. Effect of thin-layering on seismicreflectivity: Estimation of sand/shale ratio using stochastic simulation andBayes' inversion. SEG Expanded Abstracts 18: 1787-1790.DOI:10.1190/1.1820885.
Takahashi, I., Mukerji, T., and Mavko, G. 1999b. A strategy to select optimal seismicattributes for reservoir property estimation: Application of informationtheory. SEG Expanded Abstracts 18: 1584-1587.DOI:10.1190/1.1820828.
Waggoner, J.R. 2000a. LessonsLearned From 4D Projects. SPEREE 3 (4): 310-318.SPE-65369-PA. DOI: 10.2118/65369-PA.
Waggoner, J.R. 2000b. Quantifying the Economic Impact of 4DSeismic. Paper SPE 63133 presented at the SPE Annual Technical Conferenceand Exhibition, Dallas, 1-4 October. DOI: 10.2118/63133-MS.
Waggoner, J.R. 2002. Quantifying the Economic Impact of 4DSeismic Projects. SPEREE 5 (2): 111-115. SPE-77969-PA. DOI:10.2118/77969-PA.
Wang, Z., Cates, M.E., and Langan, R.T. 1998. Seismic monitoring of aCO2 flood in a carbonate reservoir: A rock physics study.Geophysics 63 (5): 1604-1617. DOI:10.1190/1.1444457.