Modern 3-D seismic interpretation

Dorn, Geoffrey A.

doi:10.1190/1.1438121

Cited by 90 publications

(28 citation statements)

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“…The most commonly employed tech-nique for horizon tracking is the so called autotracking or autopicking [3]. These algorithms require manually selected seed points and search for similar features on a neighbouring trace.…”

Section: Automated Structural Seismic Interpretationmentioning

confidence: 99%

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The Application of Genetic Algorithms in Structural Seismic Image Interpretation

Aurnhammer

Tönnies

2002

Lecture Notes in Computer Science

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Abstract. In this paper, we examine the applicability and repeatability of a genetic algorithm to automatically correlate horizons across faults in seismic data images. This problem arises from geological sciences where it is a subtask of the structural interpretation of seismic images. Because of the small amount of local information contained in those types of images, we developed a geological model in order to reduce interpretation uncertainties. The key problem is an optimisation task which cannot be solved exhaustively since it would cause exponential computational cost. Among stochastic methods, a genetic algorithm has been chosen to solve the application problem. Repeated application of the algorithm to four different faults delivered an acceptable solution in 94-100% of the experiments. The global optimum was equal to the geologically best solution in three of the four cases.

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Section: Automated Structural Seismic Interpretationmentioning

confidence: 99%

“…Computer-aided interpretation of fault surfaces is significantly less advanced than horizon interpretation [3]. Coherence measures such as cross correlation [4] or semblance [5] are applied to seismic data for imaging geological discontinuities like faults or stratigraphic features.…”

Section: Automated Structural Seismic Interpretationmentioning

confidence: 99%

The Application of Genetic Algorithms in Structural Seismic Image Interpretation

Aurnhammer

Tönnies

2002

Lecture Notes in Computer Science

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“…Several workers have carried out structural interpretation of seismic data in different sedimentary basins worldwide using 3-D seismic and well log data [16][17][18][19].Similarly, structural interpretation, petrology, provenance and depositional environment studies of the reservoir sandstones of part of the Niger Delta have been carried out to determine the hydrocarbon potentials of the area by various authors [12,14,[20][21][22][23][24][25][26][27]. Over the years, 3-D seismic and well log data have been integrated for possible generation of geologic models that often incorporate geologic, petrophysical and geophysical constraints leading to more robust interpretation of the seismic data [2][3]15].…”

Section: Introductionmentioning

confidence: 99%

“…Seismic attributes are characteristics of a seismic data often represented by analytical maps that aids the interpreter in better interpretation and visualization of geological features of interest. Seismic attributes have evolved over the past three decades and have been invaluable in making far better accurate predictions and characterization of reservoir properties [2][3][4][5][6]. They are specifically applied in hydrocarbon exploration and development [6].…”

Section: Introductionmentioning

confidence: 99%

3-D Seismic Interpretation and Volumetric Estimation of “Osaja Field” Niger Delta, Nigeria

Osaki

Opara

Okereke

et al. 2016

ILNS

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3-D seismic interpretation and petrophysical analysis of the Osaja Field, Niger Delta, was carried out with aim of carrying out a detailed structural interpretation, reservoir characterization and volumetric estimation of the field. Four wells were correlated across the field to delineate the lithology and establish the continuity of reservoir sand as well as the general stratigraphy of the area. The petrophysical analysis carried out, revealed two sand units that are hydrocarbon bearing reservoirs (Sand_A and Sand_B).The spatial variation of the reservoirs were studied on a field wide scale using seismic interpretation. Time and depth structural maps generated were used to establish the structural architecture/geometry of the prospect area of the field. The depth structure map revealed NE-SW trending anticlinal structures with F5and F6as faults assisted closures to the reservoir. Furthermore, reservoir parameters such as net pay, water saturation porosity, net-to-gross etc, were derived from the integration of seismic and well log data. The structural interpretation on the 3-D seismic data of the study area revealed a total of seven faults ranging from synthetic to antithetic faults. The petrophysical analysis gave the porosity values of the reservoir Sand_A ranging from 18.1 - 20.3% and reservoir Sand_B ranging from 13.1-14.9% across the reservoir. The permeability values of reservoir Sand_A ranging from 63-540md and reservoir Sand_B ranging from 18-80md hence there is decrease in porosity and permeability of the field with depth.The net-to-gross varies from 22.1% to 22.4% in Rerservoir Sand A to between 5.34- 12% for Rerservoir Sand _A while Sw values for the reservoirs ranges from 38-42% in well 2 to about 68.79-96.06% in well 11. The result of original oil in place for all the wells calculated revealed that well 2 has the highest value with 9.3mmbls. These results indicate that the reservoirs under consideration have a poor to fair hydrocarbon (oil) prospect.

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“…Auto-picking or auto-trackers (reviewed in [2], and [8]) have been commonly used to assist horizon tracking. Autopicking tools are aimed at extending manually selected seismic traces based on local similarity measures.…”

Section: Introductionmentioning

confidence: 99%