news West African deep water initiatives Scalable simulator for large projects Awilco acquision puts PGS on FPSO leader board Two reservoir products Contract briefs Refraction statics made easy! Company divides and rules OBC survey for Europoort restricted zone Apatite fission tracking 10 years on 3D models for reservoir management tool Western alliance for Apache Kazak report foreshadows licensing round Western Siberia debut for Russian survey group Gulf of Oman survey in Pakistan EEZ Expansion moves by grav/mag specialist Reservoir characterization software update Production data for Finder system 3D prestack depth migration service Contractors attack exploration rules Australian contractors cry foul on exported data Rugged systems for deep mapping People in the News

First Break ◽  
1997 ◽  
Vol 15 (11) ◽  
pp. 345-353
Keyword(s):  
Deep Water ◽  
Western Siberia ◽  
Reservoir Characterization ◽  
3D Models ◽  
West African ◽  
Management Tool ◽  
Production Data ◽  
Software Update ◽  
Gulf Of Oman ◽  
Depth Migration

The Influence of Main Factors on Deepwater Sediments

2021 ◽  
Author(s):  
Anton Khitrenko ◽  
Adelia Minkhatova ◽  
Vladimir Orlov ◽  
Dmitriy Kotunov ◽  
Salavat Khalilov
Keyword(s):  
Sequence Stratigraphy ◽  
Deep Water ◽  
Western Siberia ◽  
Reservoir Characterization ◽  
Water Reservoir ◽  
Shelf Break ◽  
Sediment Supply ◽  
Reservoir Quality ◽  
Geological Uncertainty ◽  
Forced Regression

Abstract Western Siberia is a unique petroleum basin with exclusive geological objects. Those objects allow us to test various methods of sequence stratigraphy, systematization and evaluation approaches for reservoir characterization of deep-water sediments. Different methods have potential to decrease geological uncertainty and predict distribution and architecture of deep-water sandstone reservoir. There are many different parameters that could be achieved through analysis of clinoform complex. Trajectories of shelf break, volume of sediment supply and topography of basin influence on architecture of deep-water reservoir. Based on general principles of sequence stratigraphy, three main trajectories changes shelf break might be identified: transgression, normal regression and forced regression. And each of them has its own distinctive characteristics of deepwater reservoir. However, to properly assess the architecture of deepwater reservoir and potential of it, numerical characteristics are necessary. In our paper, previously described parameters were analyzed for identification perspective areas of Achimov formation in Western Siberia and estimation of geological uncertainty for unexplored areas. In 1996 Helland-Hansen W., Martinsen O.J. [5] described different types of shoreline trajectory. In 2002 Steel R.J., Olsen T. [11] adopted types of shoreline trajectory for identification of truncation termination. O. Catuneanu (2009) [1] summarize all information with implementation basis of sequence stratigraphy. Over the past decade, many geoscientists have used previously published researches to determine relationship between geometric structures of clinoforms and architecture of deep-water sediments and its reservoir quality. Significant amount of publications has allowed to form theoretical framework for the undersanding sedimentation process and geometrical configuration of clinoforms. However, there is still no relationship between sequence stratigraphy framework of clinoroms and reservoir quality and its uncertainty, which is necessary for new area evaluation.

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

Turbidite depositional architecture across three interconnected deep-water basins on the north-west African margin

Sedimentology ◽  
2002 ◽  
Vol 49 (4) ◽  
pp. 669-695 ◽  
Author(s):  
Russell B. Wynn ◽  
Philip P. E. Weaver ◽  
Douglas G. Masson ◽  
Dorrik A. V. Stow
Keyword(s):  
Deep Water ◽  
West African ◽  
North West ◽  
The North ◽  
Depositional Architecture

Assessing Bypassed Hydrocarbons

2021 ◽  
Author(s):  
Stanley Oifoghe ◽  
Nora Alarcon ◽  
Lucrecia Grigoletto
Keyword(s):  
Continental Shelf ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
West African ◽  
The Novel ◽  
The West ◽  
Present Case Study ◽  
Novel Model

Abstract Hydrocarbons are bypassed in known fields. This is due to reservoir heterogeneities, complex lithology, and limitations of existing technology. This paper seeks to identify the scenarios of bypassed hydrocarbons, and to highlight how advances in reservoir characterization techniques have improved assessment of bypassed hydrocarbons. The present case study is an evaluation well drilled on the continental shelf, off the West African Coastline. The targeted thin-bedded reservoir sands are of Cenomanian age. Some technologies for assessing bypassed hydrocarbon include Gamma Ray Spectralog and Thin Bed Analysis. NMR is important for accurate reservoir characterization of thinly bedded reservoirs. The measured NMR porosity was 15pu, which is 42% of the actual porosity. Using the measured values gave a permeability of 5.3mD as against the actual permeability of 234mD. The novel model presented in this paper increased the porosity by 58% and the permeability by 4315%.

Reservoir characterization of a Permian deep-water sandstone, East Ford field, Delaware basin, Texas

AAPG Bulletin ◽  
2003 ◽  
Vol 87 (4) ◽  
pp. 609-627 ◽  
Author(s):  
Shirley P. Dutton ◽  
William A. Flanders ◽  
Mark D. Barton
Keyword(s):  
Deep Water ◽  
Reservoir Characterization ◽  
Delaware Basin

Ultra-deepwater seismic plays offshore Brazil — Future drilling off Santos and Campos Basins

2019 ◽  
Vol 7 (4) ◽  
pp. SH99-SH109
Author(s):  
Roberto Fainstein ◽  
Ana Krueger ◽  
Webster Ueipass Mohriak
Keyword(s):  
Data Acquisition ◽  
Reservoir Characterization ◽  
Carbonate Reservoirs ◽  
Source Rocks ◽  
Brazilian Coast ◽  
Depth Migration ◽  
3D Data ◽  
Shelf Slope ◽  
Seismic Amplitudes

Contemporaneous seismic data acquisition in the Santos and Campos Basins offshore Brazil have focused on image characterization of deepwater and ultra-deepwater reservoirs and their relationship with hydrocarbons originating from synrift source rocks. Our interpretation has mapped the stratigraphy of postsalt turbidite reservoirs, and, on the presalt lithology, it has uncovered the underlying synrift sequences that embrace oil-bearing source rocks and the prolific, recently discovered, microbialite carbonate reservoirs. The new phase in geophysical data acquisition and offshore drilling that started in 1999 bolstered the Brazilian offshore petroleum production to record levels. The new, massive, nonexclusive, speculative 2D and 3D data acquisition surveys conducted offshore the Brazilian coast far exceed the amount of all existing cumulative vintage data. Deepwater drilling programs probed the interpreted new prospects. As whole, the modern geophysics data libraries offshore Brazil brought in the technology era to seismic interpretation, reservoir characterization, and geosteering operations in deepwater development drilling. Still, regional interpretation mapping of the outer shelf, slope, deepwater and ultra-deepwater provinces of the Santos and Campos Basins indicates plenty of prospective future drilling in the salt locked minibasins of the ultra-deepwater provinces. Salt tectonics shapes the architecture of these basins; hence, postsalt deepwater turbidite plays were readily interpreted from seismic amplitudes of the modern data that also allow for resolution images of the synrift source rocks, salt architecture, migration paths through faulting and salt windows, reservoir characterization, and regional seal mapping. The new techniques of prestack depth migration have enabled uncovering the imaging structure of the synrift that led to characterization of the presalt carbonate reservoirs and discovery of giant accumulations. Future offshore exploration will continue aiming at postsalt deepwater and ultra-deepwater minibasins plus presalt plays under the massive salt walls, still an underexplored frontier.

VSP survey assists in the reservoir characterization of deep-water turbiditic reservoir offshore Brazil

2011 ◽  
Author(s):  
João José Marques* ◽  
Vitor Novelino ◽  
Rafael Guerra ◽  
Mario Galaguza ◽  
Monica Costa
Keyword(s):  
Deep Water ◽  
Reservoir Characterization
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