Buzios Drainage Strategy: A Combination Of Reservoir Characterization, Risks Mitigation And Unique Contract Features

2021 ◽  
Author(s):  
Victor Silva ◽  
Ana Moliterno ◽  
Carlos Henrique Araujo ◽  
Francis Pimentel ◽  
Jose Ronaldo Melo ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Deep Water ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Dynamic Modelling ◽  
Seismic Survey ◽  
Ocean Bottom ◽  
Sweep Efficiency ◽  
Tax Regimes ◽  
The Right

Abstract Petrobras acquired the right to produce 3.058 billion boe under the Transfer of Rights (ToR) in Buzios field, which still has a recoverable surplus, recently auctioned by the Brazilian Petroleum Regulatory Agency. Properly planning the production development of a supergiant field and under two tax regimes, requires a large multidisciplinary effort of data acquisition, characterization and modelling. Located in the Santos Basin Pre-Salt Pole, the Buzios field is a deep-water supergiant that has a large thickness of carbonate reservoirs, with significant areal and vertical variation. The presence of faults, fractures, karsts and other diagenetic processes adds complexity to the field, which motivated the development and implantation of industry innovations to enable its development. The presence of high levels of CO2 and H2S in the reservoir fluid, the risk of inorganic scaling and asphaltene deposition and risks of early fluid channeling and low sweep efficiency due to the aforementioned geological complexities are challenges that need to be addressed. One of these challenges is to ensure a better seismic data for the reservoir characterization. The 3D seismic data from a streamer acquisition did not have sufficient quality for this. The geological complexity of the field, the great reservoir depth and mainly the very irregular topography of the overlying evaporitic sequence indicated the need for rich azimuth seismic data. This led to the world's largest ultra-deep water seismic survey using Ocean Bottom Nodes (OBN) technology. This paper will address the static and dynamic data acquisition from the wells and the Early Productions Systems (EPS), as well as the challenges that arose and were faced by Petrobras through technology and innovation, and the complexity of the reservoir dynamic modelling. Furthermore, the OBN seismic acquisition in Buzios will be discussed in more detail, as well as the frontier that this acquisition opens to the development of the field.

Exploring New Zealand's marine territory

2011 ◽  
Vol 51 (1) ◽  
pp. 549 ◽  
Author(s):  
Chris Uruski
Keyword(s):  
New Zealand ◽  
Deep Water ◽  
Seismic Data ◽  
East Coast ◽  
Sedimentary Basins ◽  
Petroleum Exploration ◽  
Seismic Survey ◽  
Data Set ◽  
Eastern Australia ◽  
Deep Water Part

Around the end of the twentieth century, awareness grew that, in addition to the Taranaki Basin, other unexplored basins in New Zealand’s large exclusive economic zone (EEZ) and extended continental shelf (ECS) may contain petroleum. GNS Science initiated a program to assess the prospectivity of more than 1 million square kilometres of sedimentary basins in New Zealand’s marine territories. The first project in 2001 acquired, with TGS-NOPEC, a 6,200 km reconnaissance 2D seismic survey in deep-water Taranaki. This showed a large Late Cretaceous delta built out into a northwest-trending basin above a thick succession of older rocks. Many deltas around the world are petroleum provinces and the new data showed that the deep-water part of Taranaki Basin may also be prospective. Since the 2001 survey a further 9,000 km of infill 2D seismic data has been acquired and exploration continues. The New Zealand government recognised the potential of its frontier basins and, in 2005 Crown Minerals acquired a 2D survey in the East Coast Basin, North Island. This was followed by surveys in the Great South, Raukumara and Reinga basins. Petroleum Exploration Permits were awarded in most of these and licence rounds in the Northland/Reinga Basin closed recently. New data have since been acquired from the Pegasus, Great South and Canterbury basins. The New Zealand government, through Crown Minerals, funds all or part of a survey. GNS Science interprets the new data set and the data along with reports are packaged for free dissemination prior to a licensing round. The strategy has worked well, as indicated by the entry of ExxonMobil, OMV and Petrobras into New Zealand. Anadarko, another new entry, farmed into the previously licensed Canterbury and deep-water Taranaki basins. One of the main results of the surveys has been to show that geology and prospectivity of New Zealand’s frontier basins may be similar to eastern Australia, as older apparently unmetamophosed successions are preserved. By extrapolating from the results in the Taranaki Basin, ultimate prospectivity is likely to be a resource of some tens of billions of barrels of oil equivalent. New Zealand’s largely submerged continent may yield continent-sized resources.

Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New Zealand

2020 ◽  
Vol 39 (10) ◽  
pp. 727-733
Author(s):  
Haibin Di ◽  
Leigh Truelove ◽  
Cen Li ◽  
Aria Abubakar
Keyword(s):  
New Zealand ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Deep Convolutional Neural Networks ◽  
Seismic Attribute ◽  
Data Set ◽  
Seismic Fault ◽  
Attribute Analysis ◽  
Specific Implementation

Accurate mapping of structural faults and stratigraphic sequences is essential to the success of subsurface interpretation, geologic modeling, reservoir characterization, stress history analysis, and resource recovery estimation. In the past decades, manual interpretation assisted by computational tools — i.e., seismic attribute analysis — has been commonly used to deliver the most reliable seismic interpretation. Because of the dramatic increase in seismic data size, the efficiency of this process is challenged. The process has also become overly time-intensive and subject to bias from seismic interpreters. In this study, we implement deep convolutional neural networks (CNNs) for automating the interpretation of faults and stratigraphies on the Opunake-3D seismic data set over the Taranaki Basin of New Zealand. In general, both the fault and stratigraphy interpretation are formulated as problems of image segmentation, and each workflow integrates two deep CNNs. Their specific implementation varies in the following three aspects. First, the fault detection is binary, whereas the stratigraphy interpretation targets multiple classes depending on the sequences of interest to seismic interpreters. Second, while the fault CNN utilizes only the seismic amplitude for its learning, the stratigraphy CNN additionally utilizes the fault probability to serve as a structural constraint on the near-fault zones. Third and more innovatively, for enhancing the lateral consistency and reducing artifacts of machine prediction, the fault workflow incorporates a component of horizontal fault grouping, while the stratigraphy workflow incorporates a component of feature self-learning of a seismic data set. With seven of 765 inlines and 23 of 2233 crosslines manually annotated, which is only about 1% of the available seismic data, the fault and four sequences are well interpreted throughout the entire seismic survey. The results not only match the seismic images, but more importantly they support the graben structure as documented in the Taranaki Basin.

Reservoir characterization over the Lille Prinsen and Ivar Aasen fields in the Norwegian North Sea using ocean-bottom-node seismic data — A case study

2021 ◽  
pp. 1-64
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Mikal Trulsvik ◽  
Adriana Citlali Ramirez ◽  
David Went ◽  
...  
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Late Triassic ◽  
Ocean Bottom ◽  
Impedance Inversion ◽  
High Area ◽  
Elastic Impedance

An integrated workflow is proposed for estimating elastic parameters within the Late Triassic Skagerrak Formation, the Middle Jurassic Sleipner and Hugin Formations, the Paleocene Heimdal Formation and Eocene Grid Formation in the Utsira High area of the Norwegian North Sea. The proposed workflow begins with petrophysical analysis carried out at the available wells. Next, model-based prestack simultaneous impedance inversion outputs were derived, and attempts were made to estimate the petrophysical parameters (volume of shale, porosity, and water saturation) from seismic data using extended elastic impedance. On not obtaining convincing results, we switched over to multiattribute regression analysis for estimating them, which yielded encouraging results. Finally, the Bayesian classification approach was employed for defining different facies in the intervals of interest.

scholarly journals Estimation of reservoir properties from seismic data by smooth neural networks

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1969-1983 ◽  
Author(s):  
M. M. Saggaf ◽  
M. Nafi Toksöz ◽  
H. M. Mustafa
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Back Propagation ◽  
Reservoir Rock ◽  
Seismic Survey ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Network Parameters ◽  
Generalization Problem ◽  
Validation Tests

The performance of traditional back‐propagation networks for reservoir characterization in production settings has been inconsistent due to their nonmonotonous generalization, which necessitates extensive tweaking of their parameters in order to achieve satisfactory results and avoid overfitting the data. This makes the accuracy of these networks sensitive to the selection of the network parameters. We present an approach to estimate the reservoir rock properties from seismic data through the use of regularized back propagation networks that have inherent smoothness characteristics. This approach alleviates the nonmonotonous generalization problem associated with traditional networks and helps to avoid overfitting the data. We apply the approach to a 3D seismic survey in the Shedgum area of Ghawar field, Saudi Arabia, to estimate the reservoir porosity distribution of the Arab‐D zone, and we contrast the accuracy of our approach with that of traditional back‐propagation networks through cross‐validation tests. The results of these tests indicate that the accuracy of our approach remains consistent as the network parameters are varied, whereas that of the traditional network deteriorates as soon as deviations from the optimal parameters occur. The approach we present thus leads to more robust estimates of the reservoir properties and requires little or no tweaking of the network parameters to achieve optimal results.

Combining Saturation Changes and 4D Seismic for Updating Reservoir Characterizations

2006 ◽  
Vol 9 (05) ◽  
pp. 502-512 ◽  
Author(s):  
Arne Skorstad ◽  
Odd Kolbjornsen ◽  
Asmund Drottning ◽  
Havar Gjoystdal ◽  
Olaf K. Huseby
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
History Matching ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Production Data ◽  
Reservoir Model ◽  
Simple Method ◽  
4D Seismic ◽  
Elastic Inversion

Summary Elastic seismic inversion is a tool frequently used in analysis of seismic data. Elastic inversion relies on a simplified seismic model and generally produces 3D cubes for compressional-wave velocity, shear-wave velocity, and density. By applying rock-physics theory, such volumes may be interpreted in terms of lithology and fluid properties. Understanding the robustness of forward and inverse techniques is important when deciding the amount of information carried by seismic data. This paper suggests a simple method to update a reservoir characterization by comparing 4D-seismic data with flow simulations on an existing characterization conditioned on the base-survey data. The ability to use results from a 4D-seismic survey in reservoir characterization depends on several aspects. To investigate this, a loop that performs independent forward seismic modeling and elastic inversion at two time stages has been established. In the workflow, a synthetic reservoir is generated from which data are extracted. The task is to reconstruct the reservoir on the basis of these data. By working on a realistic synthetic reservoir, full knowledge of the reservoir characteristics is achieved. This makes the evaluation of the questions regarding the fundamental dependency between the seismic and petrophysical domains stronger. The synthetic reservoir is an ideal case, where properties are known to an accuracy never achieved in an applied situation. It can therefore be used to investigate the theoretical limitations of the information content in the seismic data. The deviations in water and oil production between the reference and predicted reservoir were significantly decreased by use of 4D-seismic data in addition to the 3D inverted elastic parameters. Introduction It is well known that the information in seismic data is limited by the bandwidth of the seismic signal. 4D seismics give information on the changes between base and monitor surveys and are consequently an important source of information regarding the principal flow in a reservoir. Because of its limited resolution, the presence of a thin thief zone can be observed only as a consequence of flow, and the exact location will not be found directly. This paper addresses the question of how much information there is in the seismic data, and how this information can be used to update the model for petrophysical reservoir parameters. Several methods for incorporating 4D-seismic data in the reservoir-characterization workflow for improving history matching have been proposed earlier. The 4D-seismic data and the corresponding production data are not on the same scale, but they need to be combined. Huang et al. (1997) proposed a simulated annealing method for conditioning these data, while Lumley and Behrens (1997) describe a workflow loop in which the 4D-seismic data are compared with those computed from the reservoir model. Gosselin et al. (2003) give a short overview of the use of 4D-seismic data in reservoir characterization and propose using gradient-based methods for history matching the reservoir model on seismic and production data. Vasco et al. (2004) show that 4D data contain information of large-scale reservoir-permeability variations, and they illustrate this in a Gulf of Mexico example.

From ocean-bottom cable seismic to porosity volume: A prestack PP and PS analysis of a turbidite reservoir, deepwater Campos Basin, Brazil

2014 ◽  
Vol 2 (2) ◽  
pp. SE91-SE103 ◽  
Author(s):  
Luiz M. R. Martins ◽  
Thomas L. Davis
Keyword(s):  
Seismic Reflection ◽  
Reservoir Characterization ◽  
Seismic Attributes ◽  
Seismic Survey ◽  
Ocean Bottom ◽  
Campos Basin ◽  
Coastal Basins ◽  
Porosity Logs ◽  
Elastic Impedance ◽  
Multicomponent Data

The Campos Basin is the best known and most productive of the Brazilian coastal basins. Turbidites are, by far, the main hydrocarbon-bearing reservoirs in the Campos Basin. Using a 4C ocean-bottom cable seismic survey, we set out to improve the reservoir characterization in a deepwater turbidite field in the Campos Basin. To achieve our goal, prestack angle gathers were derived and PP and PS inversion were performed. The inversion was used as an input to predict the petrophysical properties of the reservoir. Converting seismic reflection amplitudes into impedance profiles not only maximizes vertical resolution but also minimizes tuning effects. Mapping the porosity is extremely important in the development of hydrocarbon reservoirs. Combining seismic attributes derived from the PP and PS multicomponent data and porosity logs, we used linear multiregression and neural networking to predict porosity between the seismic attributes and porosity logs at the well locations. After estimating porosity in the well locations, those relationships were applied to the seismic attributes to generate a 3D porosity volume. The porosity volume highlighted the best reservoir facies in the reservoir. The integration of elastic impedance, shear impedance, and porosity improved the reservoir characterization.

Seismic-petrophysical reservoir characterization in the northern part of the Chicontepec Basin, Mexico

2016 ◽  
Vol 4 (3) ◽  
pp. T403-T417 ◽  
Author(s):  
Supratik Sarkar ◽  
Sumit Verma ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Rock Properties ◽  
Well Log ◽  
Third Order ◽  
East Central ◽  
Well Control ◽  
Seismic Geomorphology ◽  
Tight Reservoirs

The Chicontepec Formation in east-central Mexico is comprised of complex unconventional reservoirs consisting of low-permeability disconnected turbidite reservoir facies. Hydraulic fracturing increases permeability and joins these otherwise tight reservoirs. We use a recently acquired 3D seismic survey and well control to divide the Chicontepec reservoir interval in the northern part of the basin into five stratigraphic units, equivalent to global third-order seismic sequences. By combining well-log and core information with principles of seismic geomorphology, we are able to map deepwater facies within these stratigraphic units that resulted from the complex interaction of flows from different directions. Correlating these stratigraphic units to producing and nonproducing wells provides the link between rock properties and Chicontepec reservoirs that could be delineated from surface seismic data. The final product is a prestack inversion-driven map of stacked pay that correlates to currently producing wells and indicates potential untapped targets.

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