Pre-stack sea bottom detection and swell correction within an expected hyperbolic range for noisy high-resolution 8-channel airgun seismic data

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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Masses and ages for metal-poor stars

Astronomy and Astrophysics ◽

10.1051/0004-6361/201834081 ◽

2019 ◽

Vol 627 ◽

pp. A173 ◽

Cited By ~ 4

Author(s):

M. Valentini ◽

C. Chiappini ◽

D. Bossini ◽

A. Miglio ◽

G. R. Davies ◽

...

Keyword(s):

High Resolution ◽

Seismic Data ◽

Age Determination ◽

High Resolution Spectroscopy ◽

Chemical Abundances ◽

Abundance Pattern ◽

Halo Stars ◽

Stellar Ages ◽

First Results ◽

Detailed Chemical

Context. Very metal-poor halo stars are the best candidates for being among the oldest objects in our Galaxy. Samples of halo stars with age determination and detailed chemical composition measurements provide key information for constraining the nature of the first stellar generations and the nucleosynthesis in the metal-poor regime. Aims. Age estimates are very uncertain and are available for only a small number of metal-poor stars. We present the first results of a pilot programme aimed at deriving precise masses, ages, and chemical abundances for metal-poor halo giants using asteroseismology and high-resolution spectroscopy. Methods. We obtained high-resolution UVES spectra for four metal-poor RAVE stars observed by the K2 satellite. Seismic data obtained from K2 light curves helped improve spectroscopic temperatures, metallicities, and individual chemical abundances. Mass and ages were derived using the code PARAM, investigating the effects of different assumptions (e.g. mass loss and [α/Fe]-enhancement). Orbits were computed using Gaia DR2 data. Results. The stars are found to be normal metal-poor halo stars (i.e. non C-enhanced), and an abundance pattern typical of old stars (i.e. α and Eu-enhanced), and have masses in the 0.80−1.0 M⊙ range. The inferred model-dependent stellar ages are found to range from 7.4 Gyr to 13.0 Gyr with uncertainties of ∼30%−35%. We also provide revised masses and ages for metal-poor stars with Kepler seismic data from the APOGEE survey and a set of M4 stars. Conclusions. The present work shows that the combination of asteroseismology and high-resolution spectroscopy provides precise ages in the metal-poor regime. Most of the stars analysed in the present work (covering the metallicity range of [Fe/H] ∼ −0.8 to −2 dex) are very old >9 Gyr (14 out of 19 stars), and all of the stars are older than >5 Gyr (within the 68 percentile confidence level).

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