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

Abstract

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).

Impact of the Non-repeatability of Sources and Receivers in Ocean Bottom Acquisitions - a Synthetic 2D Study in Santos Basin Pre-salt

“…Although source mispositioning also had strong effect on NRMS for our study, our experience with processing of field data in the receiver domain shows that a dense shooting grid allows for a good wavefield reconstruction in a regular grid, which could severely reduce this non-repeatability impact. Since in practice the sources can be better handled during processing (and also because there are no currently engineering solutions for achieving actual perfect repeatability on the source side), the efforts to reduce the NRMS should be focused on improving receiver repeatability -via reducing OBN positioning uncertainty (Hatchell et al, 2019) or with the use of PRM systems (Thedy et al, 2013;Ebaid et al, 2017), for example. While the differences in the values in Table 1 seem small, even a minor NRMS improvement can significantly expand our ability to detect the time-lapse signal in the area (Mello et al, 2019).…”

Fully Autonomous Marine Seismic Acquisition Systems for Reservoir Monitoring

“…Permanent Reservoir Monitoring (PRM) systems comprise ocean bottom cables permanently installed on the seabed containing sensors, typically at 100m spacing along the cable, with the cables themselves arranged to cover the area of interest, and the whole system connected to a surface vessel, such as a FPSO. PRM systems installed in deepwater in the Brazilian Campos Basin at O-North (Ebaid, Wang, Seixas, Kumar, & et al, 2017) and Jubarte (Thedy & et al, 2015) have provided very high quality 4D seismic data. Here we consider fiber-optic systems, which are expected to be more reliable in the long term.…”

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

“…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).…”