Crosswell seismic imaging in the Buena Vista Hills, San Joaquin Valley: A case history

Langan, Robert T.; Julander, Dale; Morea, Michael F.; Addington, Carl; Lazaratos, Spyros

doi:10.1190/1.1820428

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…The coefficients for the Chebyshev polynomials are found by singular value decomposition of the log correlation data. Figure 2 shows a real example of different order surfaces fit to horizon picks from a series of five deviated wells in Chevron's Buena Vista Hills field in Kern County, California (Langan et al, 1998). The horizon picks from wireline logs are shown as the three boxes in each well in Figure 2a.…”

Section: Model Formulationmentioning

confidence: 99%

Crosswell traveltime tomography in three dimensions

2002

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Conventional crosswell direct‐arrival traveltime tomography solves for velocity in a 2‐D slice of the subsurface joining two wells. Many 3‐D aspects of real crosswell surveys, including well deviations and out‐of‐well‐plane structure, are ignored in 2‐D models. We present a 3‐D approach to crosswell tomography that is capable of handling severe well deviations and multiple‐profile datasets. Three‐dimensional pixelized models would be even more seriously underdetermined than the pixelized models that have been used in 2‐D tomography. We, therefore, employ a thinly layered, vertically discontinuous 3‐D velocity model that greatly reduces the number of model parameters. The layers are separated by 2‐D interfaces represented as 2‐D Chebyshev polynomials that are determined using a priori structural information and remain fixed in the traveltime inversion. The velocity in each layer is also represented as a 2‐D Chebyshev polynomial. Unlike pixelized models that provide limited vertical resolution and may be overparameterized horizontally, this 3‐D model provides vertical resolution comparable to the scale of wireline logs, and reduces the degrees of freedom in the horizontal parameterization to the expected in‐line and out‐of‐well‐plane horizontal resolution available in crosswell traveltime data. Ray tracing for the nonlinear traveltime inversion is performed in three dimensions. The 3‐D tomography problem is regularized using penalty constraints with a continuation strategy that allows us to extrapolate the velocity field to a 3‐D region containing the 2‐D crosswell profile. Although this velocity field cannot be expected to be accurate throughout the 3‐D region, it is at least as accurate as 2‐D tomograms near the well plane of each 2‐D crosswell profile. Futhermore, multiple‐profile crosswell data can be inverted simultaneously to resolve better the 3‐D distribution of velocity near the profiles. Our velocity parameterization is quite different from pixelized models, so resolution properties will be different. Using wave‐modeled synthetic data, we find that near horizontal raypaths have the largest mismatch between ray‐traced traveltimes and traveltimes estimated from the data. In conventional tomography, horizontal raypaths are essential for high vertical resolution. With our model, however, the highest resolution and most accurate inversions are achieved by excluding raypaths that travel nearly parallel to the geologic layering. We perform this exclusion in both a static and model‐based manner. We apply our 3‐D method to a multiple‐profile crosswell survey at the Cymric oil field in California, an area of very steep structural dips and significant well trajectory deviations. Results of this multiple‐profile 3‐D tomography correlate very well with the independently‐processed single profile results, with the advantage of an improved tie at the common well.

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Section: Model Formulationmentioning

confidence: 99%

Crosswell traveltime tomography in three dimensions

2002

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“…Several characterization studies have been conducted in this field. 21,22,23 One of the major goals of these studies has been to determine the technical feasibility of implementing a CO 2 enhanced oil recovery project in the Antelope Shale. Figure 8 shows the proposed CO 2 pilot area on the flank of an anticline, but near the crest.…”

Section: Field Case Studymentioning

confidence: 99%

“…Figure 8 shows the proposed CO 2 pilot area on the flank of an anticline, but near the crest. 22 The five wells in this area form a "five-spot" pattern. San Joaquin Basin is estimated to contain over 7 billion barrels of oil in place of which only 6% has been produced.…”

Section: Field Case Studymentioning

confidence: 99%

Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

Idrobo¹,

Malallah²,

Datta‐Gupta³

et al. 1999

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractOne of the goals of reservoir characterization, particularly in mature reservoirs, is to identify unswept regions containing high oil saturation for targeted infill drilling or enhanced recovery. A common approach to the problem is to generate high resolution distribution of reservoir properties such as permeability and porosity and then conduct flow simulations to identify regions of high oil saturation. One possible alternative to flow simulations would be to generate spatial distribution of properties that are related to fluid saturations and then infer fluid saturation distribution through the use of appropriate correlations.In this paper we present a field application to infer interwell water saturation distribution by combining crosswell seismic and well data. First, we use a non-parametric transformational approach to correlate sonic velocity with resistivity and porosity at the wells. An iterative procedure using alternating conditional expectations (ACE) forms the basis for this calibration. Next, stochastic cosimulation is carried out to generate conditional realizations of resistivity and porosity in the interwell region. In this cosimulation, cross-well seismic velocity is considered as the secondary data while resistivity and porosity are treated as primary data. Finally, water saturation distribution is deduced from the resistivity and porosity distributions through the use of Archie's law.

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Section: Field Case Studymentioning

confidence: 99%

Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

Idrobo

Malallah

Datta‐Gupta

et al. 1999

SPE Annual Technical Conference and Exhibition

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One of the goals of reservoir characterization, particularly in mature reservoirs, is to identify unswept regions containing high oil saturation for targeted infill drilling or enhanced recovery. A common approach to the problem is to generate high resolution distribution of reservoir properties such as permeability and porosity and then conduct flow simulations to identify regions of high oil saturation. One possible alternative to flow simulations would be to generate spatial distribution of properties that are related to fluid saturations and then infer fluid saturation distribution through the use of appropriate correlations. In this paper we present a field application to infer interwell water saturation distribution by combining crosswell seismic and well data. First, we use a non-parametric transformational approach to correlate sonic velocity with resistivity and porosity at the wells. An iterative procedure using alternating conditional expectations (ACE) forms the basis for this calibration. Next, stochastic cosimulation is carried out to generate conditional realizations of resistivity and porosity in the interwell region. In this cosimulation, cross-well seismic velocity is considered as the secondary data while resistivity and porosity are treated as primary data. Finally, water saturation distribution is deduced from the resistivity and porosity distributions through the use of Archie's law.

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Crosswell seismic imaging in the Buena Vista Hills, San Joaquin Valley: A case history

Cited by 7 publications

References 2 publications

Crosswell traveltime tomography in three dimensions

Crosswell traveltime tomography in three dimensions

Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

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