4D Seismic Detectability in a Brazilian Pre-Salt Carbonate Field

2021 ◽  
Author(s):  
Cyril Agut ◽  
Tom Blanchard ◽  
Ya-Hui Yin ◽  
Adeoye Adeyemi

Abstract This paper is dedicated to a pre-salt carbonate field located within the Santos Basin, Brazil, comprising thick Aptian reservoirs interspersed with igneous rocks. One of the main challenges for reservoir management is the surface constraint on the gas, as all of the produced gas will have to be reinjected and can be miscible with the in-situ hydrocarbons. The recovery mechanism selected is mainly WAG (water alternating gas) injection, with both producers and injectors equipped with intelligent completions using Inflow Control Valves (ICVs). A 4D seismic monitoring survey is planned to delineate gas and water fronts in reservoir flow units about 10m thick, providing critical information to help piloting a planned 6-month WAG cycle for improved recovery. Seismic imaging is challenging in this case and 4D signal is expected to be weak (±2% dIp/Ip). We propose here, a methodology, based on a 1-D Gassmann fluid substitution model at wells (only limited reservoir fluid PVT data available) to rapidly answer the following pertinent questions as posed by the asset team in charge of the field: From a phenomenological stand-point and neglecting some possible processing, imaging and acquisition challenges, will 4D data (post 4D inversion) detect a gas streak from an injector to a producer? What is the 4D seismic detection limit based on reservoir thickness? What kind of seismic acquisition will assure this detectability? Under the assumptions made in this work, this methodology shows that a permanent system of acquisition seems to be a fit-for-purpose technology for detectability. Further work is however recommended using full complement of a 3D static and dynamic simulation model coupled with a complete fluid PVT model in order to assess more complex 3D dynamic interactions between the injectors and producers.

2003 ◽  
Vol 9 (1) ◽  
pp. 83-90 ◽  
Author(s):  
M. Lygren ◽  
K. Fagervik ◽  
T.S. Valen ◽  
A. Hetlelid ◽  
G. Berge ◽  
...  

2002 ◽  
Vol 5 (04) ◽  
pp. 295-301
Author(s):  
Onno van Kessel

Summary The Champion East area offshore Brunei Darussalam consists of approximately 50 stacked, shallow, and intensely faulted heavy oil reservoirs. These reservoirs have been under development since 1975 and have to date produced just 9% of the oil initially in place. Over the period 1998-2003, Brunei Shell Petroleum (BSP) is embarking on a major redevelopment with the aim of converting a further 30 million m3 of oil-in-place volume into commercial reserves. An overview will be given of how new technology is adding value to the total redevelopment, supported by actual application results and learning points. The primary development of Champion East is now nearing completion. The use of existing facilities and ultra shallow, long reach horizontal wells - with innovative sand exclusion and downhole intelligence - has achieved a 60% unit cost reduction over previous drilling campaigns in the area. The only way to unlock another 5 to 15% of the oil-in-place volume is to start secondary recovery through water injection, in combination with the use of electric submersible pumps (ESPs). Introduction The Champion Asset comprises the Champion Field offshore Brunei Darussalam (Fig. 1) and all associated facilities and infrastructure, which also serve as an export hub for BSP's entire Offshore East production division. Oil production from the Champion Field averages approximately one-third of total BSP production. A large scope for recovery, mostly technology-driven, remains, even at low oil prices. Subsurface, the area comprises a hydrostatic, heterolithic sequence of interbedded thin sandstones and mudstones (with reservoir flow units no more than 15 m thick and permeabilities ranging from 0.01 to 0.2 µm2 in lower shoreface sands to 0.5 to 5 µm2 in tidal channels) deposited in environments spanning a systems tract that extends from the outer shelf into the lower coastal plain. Other key features are significant lateral thickness variations, compartmentalization caused by syndepositional tectonics, and the presence of multiple growth faults. The Champion field can be divided into two distinct parts (Fig. 2): Champion East, spanning a depth of approximately 200 to 1200 m, with hydrocarbons in some places seeping through the seabed and feeding a coral reef; and Champion Main, which encompasses a depth of approximately 1000 to 2000 m. Champion Main contains the mature core of the Champion field, where both primary and secondary (water-injection) recovery processes are well advanced and 28% of the oil initially in place has been produced. The main focus in Champion Main is on water-injection maintenance, production-system optimization, and scope for recompleting or sidetracking existing wells-all aimed at slowing the decline in oil production. Most efforts in the area are, however, focused on the growth potential offered by shallow reservoirs. The Champion East area is much less mature than Champion Main, with a cumulative oil production to date of just 9% of the oil initially in place. Historically, Champion East is underdeveloped because of its subsurface complexity and heterogeneity (leading to erratic well performance), less favorable reservoir and oil properties [density of 930 g/cm3 (20° API) and viscosity between 5 and 15 mPa's], and a perceived lack of spare conductor slots, which would necessitate large investments in new infrastructure. In 1995, it was estimated that an upfront investment in excess of U.S. $400 million would be required to advance the development of Champion East by accessing another 30 million m3 of undeveloped reserves. Out of this total, 40% would be required for new facilities, and the remaining 60% would be for drilling new wells. This hurdle essentially halted further developments (between 1992 and 1997, just one well was drilled in the area), and it was obvious that major changes were required to all the fundamentals (average reserves and rates per well, well costs, and facilities costs) to break this deadlock. The case for change, together with plans for possible solutions, is further described in Ref. 1. Reservoir Modeling Technology Traditionally, Champion East had been modeled with 2D methods of mapping gross interval properties for groups of reservoirs ranging in thickness from 20 to 40 m, using the previous 3D seismic survey shot in 1983 (relatively poor resolution) and well correlation methods based on lithostratigraphy. However, these methods often can prove unreliable in deltaic reservoirs that have undergone synsedimentary tectonics. The previous major Champion East infill drilling campaign (1990-92) was relatively unsuccessful because approximately 35% of all target reservoirs were found to be either nonexistent, water-bearing, or depleted. It then became clear that it was necessary to understand the structure, sequence stratigraphy, and fluid distribution of these reservoirs in greater detail. Two key data acquisition activities occurred in 1994: a high-resolution 3D seismic survey and the retrieval of some 350 m of continuous cores to review the sedimentology and high-resolution sequence stratigraphy, as described in Ref. 2. After screening studies to establish the correct priority and level of detail required, Shell's proprietary reservoir modeling software (GEOCAP-MoReS) was used to provide detailed 3D reservoir models for reservoir simulation. A total of 16 models were built and history matched (with approximately 50,000 grid cells each) between 1996 and 1999; together, they covered the entire area, with boundaries positioned (generally at sealing faults) to minimize crossflow effects. This allowed fast optimization of reservoir development plans by identifying connected oil in place and transmissibility for individual reservoir flow units, such as an upper shoreface sandbody or a tidal channel, which have remained undrained from previous development.


Author(s):  
G. O. Emujakporue ◽  
E. E. Enyenihi

In this study, the flow units of reservoirs of Akos field have been computed with the Stratigraphic Modified Lorenz plot. Cumulative flow capacity and cumulative storage capacity were used for constructing the Stratigraphic Modified Lorenz Plot (SMLP). The flow capacity and storage capacity are functions of calculated permeability and porosity values considering their sampling depth. The porosity and permeability were obtained from composite well logs of eight oil wells in the study area. Two reservoirs A and B were delineated from the well logs. The stratigraphic Modified Lorenz Plots (SMLP) revealed a total of one hundred and twelve (112) Flow units (FU) in the two observed reservoirs A and B. Reservoir A has a total of 53 FU (25 speed zones, 18 baffle zones and 10 barrier zones) and reservoir B has a total of 59 FU (29 speed zones, 16 baffle zones and 14 barrier zones) which cut across all the wells. The flow units in both reservoirs fall within the speed zones, baffles and barrier unit categories. The speed zone units with equal flow and storage capacities are the dominant flow units in both reservoirs. This is an indication that the sediments have good reservoir qualities. The baffle zones have more storage capacity than the speed zones. The barrier zones within the reservoirs are acting as a seal to the flow of fluid.


2014 ◽  
Vol 41 (5) ◽  
pp. 634-641 ◽  
Author(s):  
Zifei FAN ◽  
Kongchou LI ◽  
Jianxin LI ◽  
Heng SONG ◽  
Ling HE ◽  
...  

2010 ◽  
Vol 13 (01) ◽  
pp. 37-43 ◽  
Author(s):  
John R. Fanchi

Summary Time-lapse (4D) seismic can be effectively integrated into the reservoir-management process by embedding the calculation of seismic attributes in a flow simulator. This paper describes a petroelastic model (PEM) embedded in a multipurpose flow simulator. The flow simulator may be used to model gas, black-oil, compositional, and thermal systems. The PEM can calculate reservoir geophysical attributes such as compressional-wave (P-wave) and shear-wave (S-wave) velocities and impedances, dynamic and static Young's moduli, and dynamic and static Poisson's ratios. Examples illustrate how to use the PEM to facilitate the integration of 4D seismic and reservoir flow modeling.


2001 ◽  
Author(s):  
D. Mikes ◽  
O.H.M. Barzandji ◽  
J. Bruining ◽  
C.R. Geel

2018 ◽  
Vol 6 (3) ◽  
pp. T601-T611
Author(s):  
Juliana Maia Carvalho dos Santos ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer ◽  
Colin MacBeth

Time-lapse (or 4D) seismic attributes are extensively used as inputs to history matching workflows. However, this integration can potentially bring problems if performed incorrectly. Some of the uncertainties regarding seismic acquisition, processing, and interpretation can be inadvertently incorporated into the reservoir simulation model yielding an erroneous production forecast. Very often, the information provided by 4D seismic can be noisy or ambiguous. For this reason, it is necessary to estimate the level of confidence on the data prior to its transfer to the simulation model process. The methodology presented in this paper aims to diagnose which information from 4D seismic that we are confident enough to include in the model. Two passes of seismic interpretation are proposed: the first, intended to understand the character and quality of the seismic data and, the second, to compare the simulation-to-seismic synthetic response with the observed seismic signal. The methodology is applied to the Norne field benchmark case in which we find several examples of inconsistencies between the synthetic and real responses and we evaluate whether these are caused by a simulation model inaccuracy or by uncertainties in the actual observed seismic. After a careful qualitative and semiquantitative analysis, the confidence level of the interpretation is determined. Simulation model updates can be suggested according to the outcome from this analysis. The main contribution of this work is to introduce a diagnostic step that classifies the seismic interpretation reliability considering the uncertainties inherent in these data. The results indicate that a medium to high interpretation confidence can be achieved even for poorly repeated data.


Sign in / Sign up

Export Citation Format

Share Document