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Detecting Hydrocarbon Reservoirs from Marine CSEM in the Santos Basin, Brazil*
Marco Polo Buonora1, Andrea Zerilli2, Tiziano Labruzzo2, and Luiz Felipe Rodrigues3
Search and Discovery Article #40402 (2009)
Posted April 6, 2009
*Adapted from oral presentation at AAPG International Conference and Exhibition, Cape Town, South Africa, October 26-29, 2008
1Petrobras E&P/GEOF/MP, Rio de Janeiro, RJ,
Brazil
2WesternGeco Electromagnetics, Houston, TX, USA (mailto:[email protected]
)
3Petrobras E&P/IABS/PN, Rio de Janeiro, RJ, Brazil
The Santos Basin marine Controlled Source Electromagnetic (mCSEM) data were acquired as part of a cooperative project between Petrobras and Schlumberger to assess the integration of deep reading Electromagnetic (EM) technologies into the full cycle of oil field exploration and development. Multi-component electric and magnetic fields data were recorded. All fields at each receiver location were processed and interpreted using an advanced integrated workflow.
The main objectives of the
survey were to calibrate mCSEM over known reservoirs, quantify the anomalies
associated with those reservoirs with the expectation that new prospective
location(s) could be found. We show that the mCSEM response of the known
reservoirs yields signatures that can be imaged and accurately quantified
by new processing
and interpretation procedures. A further initiative was
to advance the state of the art in integrated interpretation and establish
guidelines toward the development of an industry standard workflow unavailable
at present.
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In recent years mCSEM has driven the attention of an increasing number of operators due to its sensitivity to map resistive structures (such as hydrocarbon reservoirs) beneath the ocean bottom, and successful case studies have been reported (Srnka and Carazzone, 2005; Darnet et al., 2007).
A few hundred commercial mCSEM surveys have been conducted in water depths ranging from 50 to 3,000+ meters and in latitudes ranging from the tropics to the Arctic. In cases where a well has been drilled in the survey area, the reservoir predictions based on the integrated interpretation have been validated. Many improvements have been made to operating practices, survey equipment and delivery of advanced answer products.
The Santos Basin survey was performed as part of a co-operation project between Petrobras and Schlumberger to assess the integration of deep reading Electromagnetic technologies into the full cycle of oil field exploration and development. The mCSEM data were acquired as a feasibility and demonstration study; to provide state of the art data, develop new insights that would lead to novel and cost effective application, establish new integrated interpretation workflows.
mCSEM
is proving to be a rewarding tool when applied to real E&P
problems, but a great deal of R&D is needed to push its efficiency
and reliability in: acquisition hardware, accurate survey engineering,
data
The
layout of the Santos Basin mCSEM survey is shown in Figures
1-2
.
One hundred and eighty mCSEM receivers spaced approx. 1 km apart,
were deployed along tow lines crossing known reservoirs in the
area. The survey used a 0.25 and 0.0625 Hz square wave signals
that are also rich in odd harmonics like 0.75, 1.25, 1.75 and
0.1875, 0.3125 and 0.4375 Hz. Data at each receiver location
was processed using an advanced workflow based on: instantaneous
dipole length, instantaneous dipole moment, instantaneous dipole
altitude, instantaneous feather angle and instantaneous
Forward multi-component E and H responses were computed for these models incorporating bathymetry and varying sea-water resistivities with water depth. Responses were computed for all frequencies used in the course of the survey.
Figure 3 shows the stacked normalized amplitude and phase centered on 5 km offset for the fundamental frequency 0.25 Hz for tow line LTAM8N. The stacked responses are normalized for the radial horizontal electric fields by the field measured at the reference receiver TAM147 (Figure 2). The choice of the reference receiver is to have the same background resistivity at the reference location and the measurement receiver location, with the only differences occurring in the possible anomalous features. The normalized fields clearly show two distinct areas of anomalies centered above two known reservoirs (A and B), reservoir A showing a maximum anomaly of about 1.8, reservoir B showing a maximum anomaly of about 1.5. The single frequency, narrow offset range data in Figure 3 gives anomalies with broad edges, but spatial resolution is enhanced by imaging with multiple frequencies and offsets. Detailed 3D modeling was carried out based on blocked well-log resistivities and model geometries derived from seismic incorporating the reservoir data. Figure 4 shows the match between the processed and stacked normalized real data and the modeled normalized response.
Selected tow lines were further imaged using a new fast 2.5D inversion method (A. Abubakar et al., 2006). The forward solution uses an optimal grid technique based on an anisotropic material averaging formula to upscale fine structure to a coarser computational grid. The algorithm allows solving the problem for multiple transmitter positions simultaneously and does not confine the sources and receivers to a single plane that is perpendicular to the invariant direction, and thus realistic acquisition geometries can be simulated. The inversion is based on a Gauss-Newton scheme with constrained minimization that enforces physical bounds on the inverted parameters via a nonlinear transformation procedure. The inverted depth images show resistivity anomalies that are consistent with the depth and lateral extent of the known reservoirs and closely tie the well-log and seismic data.
We show that
the mCSEM response of hydrocarbon reservoirs known to be present
in the Santos Basin yield anomalies that can be clearly imaged
and there are evident correlations between the anomalies and the
reservoirs. We show that the application
of a new workflow based on true geometry
There are numerous aspects that must be considered to further develop mCSEM for successful hydrocarbon exploration. One critical need will be the establishment of advanced interpretation paradigms embedded within industry-standard applications. This will become apparent as more companies start to bring mCSEM into more complex settings and potentially into production for reservoir monitoring purposes.
We thank Petrobras and WesternGeco Electromagnetics for allowing us to present this paper.
Abubakar, A., T.M. Habashy, V.L. Druskin, D. L. Alumbaugh, A. Zerilli, and L. Knizherman, 2006, Fast Two-Dimensional Forward and Inversion Algorithms for Interpreting Marine CSEM Data, OTC-18154-PP.
Darnet, M., M.C.K. Choo, R.E. Plessix, M.L. Rosenquist, K.Y. Cheong, E. Sims, and J.W.K. Voon, 2007, Detecting hydrocarbon reservoirs from CSEM data in complex settings: Application to deepwater Sabah, Malaysia, Geophysics, v. 72, no. 2.
Srnka, L.J., and J.J. Carazzone, 2005, Remote reservoir resistivity mapping – An overview, SEG International Exposition and Seventy-Fifth Annual Meeting, Houston, Texas.
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