Principal Component Analysis Applied to 3D Seismic Data for Reservoir Property Estimation

Scheevel, J.R.; Payrazyan, Karen

doi:10.2118/69739-pa

Cited by 43 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An unsupervised clustering was performed on the seismic amplitudes using PCA cluster analysis. Given a user-defined window W S of seismic data, this method, proposed by Payrazyan, 8,9 allows dividing the vector realizations W S (u) into groups, or clusters, that may relate to geological "types," for example, facies, or even depositional processes. The algorithm requires first performing a PCA on the seismic data.…”

Section: Sand Geobodies Interpreted Frommentioning

confidence: 99%

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics

2003

View full text Add to dashboard Cite

A new geostatistical approach, known as multiple-point statistics (MPS) simulation, recently has been proposed to generate 3D depositional facies models that integrate both large-scale information derived from seismic data and fine-scale information derived from well logs, cores, and analog studies. In this paper, the practicality, flexibility, and CPU advantage of this new approach are demonstrated through the modeling of an actual deepwater turbidite reservoir. First, based on well-log interpretation and a global geological understanding of the reservoir architecture, a training image depicting sinuous sand bodies is generated using a nonconditional object-based simulation algorithm. Then, disconnected sand bodies are interpreted from seismic amplitude data using a principal component cluster analysis technique, while a sand probability cube is generated using a principal component proximity transform of the same seismic. Multiple-point geostatistics allows simulating multiple realizations of channel bodies similar to the training image, constrained to the local sand probabilities, partially interpreted sand bodies, and well-log data. As illustrated in this paper, to account for uncertainty about the geometry of the sand bodies, different training images associated with alternative conceptual models proposed by the geologists can be considered.

show abstract

Section: Sand Geobodies Interpreted Frommentioning

confidence: 99%

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics

2003

View full text Add to dashboard Cite

show abstract

“…In general seismic amplitude can be used as an indicator of change of rock impedance due to a change in rock facies (shale/sand). Instead of performing a traditional seismic inversion of amplitude to impedance, we use a method proposed by Payrazyan 7,8 , named Principal Component Proximity Transform (PCPT). This method avoids the inversion step, and allows calculating directly facies probabilities from amplitudes.…”

Section: Data Sets and Prior Geological Modelsmentioning

confidence: 99%

“…An unsupervised clustering was performed on the seismic amplitudes using PCA cluster analysis. Given a user-defined window W S of seismic data, that method, proposed by Payrazyan 7,8 , allows dividing the vector realizations W S (u) into groups, or clusters, that may relate to geological "types", for example facies, or even depositional processes. The algorithm requires first performing a PCA on the seismic data.…”

Section: Data Sets and Prior Geological Modelsmentioning

confidence: 99%

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics

Strebelle¹,

Payrazyan²,

Caers³

2002

Proceedings of SPE Annual Technical Conference and Exhibition

View full text Add to dashboard Cite

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractGeological interpretation and seismic data analysis provide two complementary sources of information to model reservoir architecture. Seismic data affords the opportunity to identify geologic patterns and features at a resolution on the order of 10's of feet, while well logs and conceptual geologic models provide information at a resolution on the order of one foot. Both the large-scale distribution of geologic features and their internal fine-scale architecture influence reservoir performance. Development and application of modeling techniques that incorporate both large-scale information derived from seismic and fine-scale information derived from well logs, cores, and analog studies represents a significant opportunity to improve reservoir performance predictions.In this paper we present a practical new geostatistical approach for solving this difficult data integration problem and apply it to an actual, prominent reservoir. Traditional geostatistics relies upon a variogram to describe geologic continuity. However, a variogram, which is a two-point measure of spatial variability, cannot describe realistic, curvilinear or geometrically complex patterns. Multiple-point geostatistics uses a training image instead of a variogram to account for geological information. The training image provides a conceptual description of the subsurface geological heterogeneity, containing possibly complex multiple-point patterns of geological heterogeneity. Multiple-point statistics simulation then consists of anchoring these patterns to well data and seismic-derived information. This work introduces a novel alternative approach to traditional Bayesian modeling to incorporate seismic.The focus in this paper lies in demonstrating the practicality, flexibility and CPU-advantage of this new approach by applying it to an actual deep-water turbidite reservoir. Based on well log interpretation and a global geological understanding of the reservoir architecture, a training image depicting sinuous sand bodies is generated using a non-conditional object-based simulation algorithm. Disconnected sand bodies are interpreted from seismic amplitude data using a principal component cluster analysis technique. In addition, a map of local sand probabilities obtained from a principal component proximity transform of the same seismic is generated. Multiple-point geostatistics then simulates multiple realizations of channel bodies constrained to the local sand probabilities, partially interpreted sand bodies and well-log data. The CPU-time is comparable to traditional geostatistical methods.

show abstract

“…Large scale field applications also show that one main factor leading to prominent dissatisfaction is the systems' inability to handle uncertainty [23,27]. Each parameter or selection criterion has its own unique influence on identifying suitable candidate wells for hydraulic fracturing.…”

Section: Introductionmentioning

confidence: 99%

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

et al. 2017

View full text Add to dashboard Cite

Abstract:The selection of appropriate wells for hydraulic fracturing is one of the most important decisions faced by oilfield engineers. It has significant implications for the future development of an oilfield in terms of its productivity and economics. In this study, we developed a fuzzy model for well selection that combines the major objective criteria with the subjective judgments of decision makers. This was done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT). The AHP component was used to identify the relevant criteria involved in selecting wells for hydraulic fracturing. Grey theory was used to determine the relative importance of those criteria. Then a fuzzy expert system was applied to fuzzily process the aggregated inputs using a Type-2 fuzzy logic system. This undertakes approximate reasoning and generates recommendations for candidate wells. These techniques and technologies were hybridized by using an intercommunication job-sharing method that integrates human judgment. The proposed method was tested on data from an oilfield in Western China and finally the most appropriate candidate wells for hydraulic fracturing were ranked in order of their projected output after fracturing.

show abstract

Principal Component Analysis Applied to 3D Seismic Data for Reservoir Property Estimation

Cited by 43 publications

References 16 publications

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Contact Info

Product

Resources

About