Multiscale seismic attributes: source-corrected wavelet response and application to high-resolution seismic data

This study identified the Pleistocene depositional succession of the group (A) (marine, estuarine, and fluvial depositional systems) of the Melor and Inas fields in the central Malay Basin from the seafloor to approximately −507 ms (522 m). During the last few years, hydrocarbon exploration in Malay Basin has moved to focus on stratigraphic traps, specifically those that existed with channel sands. These traps motivate carrying out this research to image and locate these kinds of traps. It can be difficult to determine if closely spaced-out channels and channel belts exist within several seismic sequences in map-view with proper seismic sequence geomorphic elements and stratigraphic surfaces seismic cross lines, or probably reinforce the auto-cyclic aggregational stacking of the avulsing rivers precisely. This analysis overcomes this challenge by combining well-log with three-dimensional (3D) seismic data to resolve the deposition stratigraphic discontinuities’ considerable resolution. Three-dimensional (3D) seismic volume and high-resolution two-dimensional (2D) seismic sections with several wells were utilized. A high-resolution seismic sequence stratigraphy framework of three main seismic sequences (3rd order), four Parasequences sets (4th order), and seven Parasequences (5th order) have been established. The time slice images at consecutive two-way times display single meandering channels ranging in width from 170 to 900 m. Moreover, other geomorphological elements have been perfectly imaged, elements such as interfluves, incised valleys, chute cutoff, point bars, and extinction surfaces, providing proof of rapid growth and transformation of deposits. The high-resolution 2D sections with Cosine of Phase seismic attributes have facilitated identifying the reflection terminations against the stratigraphic amplitude. Several continuous and discontinuous channels, fluvial point bars, and marine sediments through the sequence stratigraphic framework have been addressed. The whole series reveals that almost all fluvial systems lay in the valleys at each depositional sequence’s bottom bars. The degradational stacking patterns are characterized by the fluvial channels with no evidence of fluvial aggradation. Moreover, the aggradation stage is restricted to marine sedimentation incursions. The 3D description of these deposits permits distinguishing seismic facies of the abandoned mud channel and the sand point bar deposits. The continuous meandering channel, which is filled by muddy deposits, may function as horizontal muddy barriers or baffles that might isolate the reservoir body into separate storage containers. The 3rd, 4th, and 5th orders of the seismic sequences were established for the studied succession. The essential geomorphological elements have been imaged utilizing several seismic attributes.

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Pitfalls and limitations in seismic attribute interpretation of tectonic features

Interpretation ◽

10.1190/int-2014-0122.1 ◽

2015 ◽

Vol 3 (1) ◽

pp. SB5-SB15 ◽

Cited By ~ 64

Author(s):

Kurt J. Marfurt ◽

Tiago M. Alves

Keyword(s):

Seismic Data ◽

Seismic Attributes ◽

Seismic Attribute ◽

3D Data ◽

Amplitude Data ◽

Seismic Sections ◽

Push Down ◽

Data Coherence ◽

Level Of Experience ◽

Tectonic Features

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.

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2D-3C high-resolution seismic data from the Abitibi Greenstone Belt, Canada

Tectonophysics ◽

10.1016/j.tecto.2008.05.038 ◽

2009 ◽

Vol 472 (1-4) ◽

pp. 226-237 ◽

Cited By ~ 5

Author(s):

David B. Snyder ◽

Peter Cary ◽

Matt Salisbury

Keyword(s):

High Resolution ◽

Seismic Data ◽

Greenstone Belt ◽

Abitibi Greenstone Belt

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Acquire Ocean Bottom Seismic Data and Time-Lapse Geochemistry Data Simultaneously to Identify Compartmentalization and Map Hydrocarbon Movement

10.4043/30975-ms ◽

2021 ◽

Author(s):

Rick Schrynemeeckers

Keyword(s):

High Resolution ◽

Seismic Data ◽

Time Lapse ◽

Field Trials ◽

Detection Methods ◽

Ocean Bottom ◽

Field Development ◽

Dense Grid ◽

Hydrocarbon Charge ◽

Sweet Spots

Abstract Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.

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Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

10.2118/207583-ms ◽

2021 ◽

Author(s):

S Al Naqbi ◽

J Ahmed ◽

J Vargas Rios ◽

Y Utami ◽

A Elila ◽

...

Keyword(s):

High Resolution ◽

Seismic Data ◽

Pore Volume ◽

Rock Physics ◽

Static Model ◽

Reservoir Quality ◽

Reservoir Properties ◽

Geostatistical Inversion ◽

High Predictability ◽

The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

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