Results of 4D Monitoring from the Deepwater BC-10 Life of Field Seismic (LoFS) in Brazil

Galarraga, M.A.; Wang, Kanglin; Farmer, Harlow G.

doi:10.1190/segam2015-5829706.1

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…Fields with PRM are still relatively uncommon, especially in deep-water settings, even though the value of these data for reservoir surveillance is well-proven. Galarraga et al (2015) detailed the first results from PRM at BC-10, and the considerable value added by time-lapse seismic data. Naturally, one of the challenges of PRM is how to smoothly and repeatedly accommodate and interpret the large volumes of incoming monitor data: Figure 3 shows a montage of the diverse and detailed types of subsurface data requiring integration.…”

Section: Data Integration Challengementioning

confidence: 99%

Practical Example of Data Integration in a PRM Environment, BC-10, Brazil

Ebaid¹,

Wang²,

Seixas³

et al. 2017

Proceedings

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The double-hulled FPSO's design required significant power and heat delivery systems to drive the seabed lift equipment and process the heavy crudes. This development is the first of its kind based on full subsea oil and gas separation and subsea pumping. This system uses 1500-horsepower underwater pumps-each equivalent to a Formula One engine-to drive oil and a small quantity of gas to the surface. Phased development The first phase of the Parque das Conchas project included the development of three fields (Abalone, Ostra and Argonauta B-West) connected to the FPSO via subsea wells and manifolds (Figure 1). The development wells were drilled by Global Santa Fe's Arctic 1 drilling rig, and the fields came on stream in July 2009. This first phase involved nine producing wells and one gas injector well. By July 2013 the project had produced more than 70 million BOE. Phase two of the project, to tie-in the Argonauta O-North field, came on stream in October 2013. In phase two an additional 11 wells were drilled (including water injection), as well as adding subsea boosting equipment and brownfield topsides upgrades on the FPSO. The estimated peak production is 35,000 BOE/d. A Permanent Reservoir Monitoring (PRM) system was installed during this phase of the project, and it is this field and PRM which are the primary focus of the present work. Phase three came on stream in March 2016. It comprises five producing wells in two fields (Massa and O-South), and two water injection wells in Massa. The fields are tied back to the FPSO. Daily production of the phase three wells is expected to total up to 20,000 barrels of oil equivalent per day at peak annual production. The success of this phased development is attributed to rigorous standardization, rapidly applied learnings, and strategic deployment of new technologies (LeBlanc et al., 2015).

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Section: Data Integration Challengementioning

confidence: 99%

Practical Example of Data Integration in a PRM Environment, BC-10, Brazil

Ebaid¹,

Wang²,

Seixas³

et al. 2017

Proceedings

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“…The workflow is applied to a heavy oil reservoir offshore Brazil where hundreds of stochastic simulations of the reservoir model are available. There is a wealth of studies that prove the advantages of 4D seismic data for a robust understanding of reservoir production activity in this heavy oil field (Buksh et al., 2015; Chen et al., 2015; Farmer et al., 2015; Galarraga et al., 2015; Ebaid et al., 2017; Maleki et al., 2019, 2021; Wang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models

Maleki

Cirne

Schiozer

et al. 2022

Geophysical Prospecting

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Time‐lapse seismic (four‐dimensional seismic) data play a preeminent role in closed‐loop reservoir management by providing a full‐field image of dynamic reservoir behaviour during production. Nonetheless, the multidisciplinary nature of four‐dimensional closed‐loop approaches demands more quantitative and fast methods to integrate rock physics models, reservoir flow simulation models and four‐dimensional seismic analysis. In this work, we tackle this time‐consuming and expensive process and develop a data‐driven quantitative approach to leverage the inherent physics between four‐dimensional seismic and reservoir property changes. We propose an inversion flow method using machine learning strategies to estimate changes in reservoir properties directly from a fast‐derived four‐dimensional seismic attribute. This study was carried out in a Brazilian deep‐water field where production started in 2013 with 3 years of production and injection history. For this reservoir, the estimation of fluid saturation maps is a critical objective to assist engineers with data assimilation procedures. We also generated millions of training data samples using 200 simulation models from the field mentioned above (before history matching) to highlight the benefits of restricting training samples to proper fluid flow consistent combinations. Results demonstrate high prediction accuracy for the targeted reservoir property changes. Additionally, it provides insights for the detection of sweet spots and positioning of infill wells. We significantly simplify the four‐dimensional seismic integration process, allowing initial engagements of reservoir engineers with decision‐making processes and data assimilation applications.

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“…4D Time-lapse seismic is a proven approach for reservoir monitoring and management. Ocean bottom node (OBN) dedicated 4D has been demonstrated as the most effective time-lapse approach to achieve high repeatability and low (<10%) NRMS (Mestayer et al 2017, Galarraga et al 2015, van Gestel et al 2013, Stopin et al 2011. The advantage of OBN dedicated 4D is that the receiver positions can be accurately repeated between vintages.…”

Section: Introductionmentioning

confidence: 99%