Analysis and validation of two mathematical models of acoustic impedance in well logs: An example in Namorado oilfield, Campos basin, Brazil

2017 ◽  
Vol 158 ◽  
pp. 739-750
Author(s):  
Mario Martins Ramos ◽  
Jorge Leonardo Martins ◽  
Rodrigo Bijani
Keyword(s):  
Mathematical Models ◽  
Acoustic Impedance ◽  
Well Logs ◽  
Campos Basin

The use of well logs in logfacies modeling – example in the Namorado Field, Campos Basin, Brazil

2005 ◽  
Author(s):  
Camila Faria de Albuquerque ◽  
José Agnelo Soares ◽  
Claudio Bettini
Keyword(s):  
Well Logs ◽  
Campos Basin

A Markov‐Gauss algorithm for blocking well logs

Geophysics ◽  
1988 ◽  
Vol 53 (8) ◽  
pp. 1118-1121 ◽  
Author(s):  
Paul N. Chouinard ◽  
Ken V. Paulson
Keyword(s):  
Electrical Resistivity ◽  
Markov Chain ◽  
Acoustic Impedance ◽  
High Probability ◽  
Rock Type ◽  
Well Logs ◽  
Physical Parameters ◽  
New Type

A characteristic common among petrophysical sections is “blockiness,” which results from the high probability that a given rock and the rocks above and below it are all of the same type. Furthermore, if a new rock type is encountered, the next rock’s type is dependent upon this new type and not on a previous type. This suggests that a Markov chain would be an appropriate basis for modeling such physical parameters as acoustic impedance, electrical resistivity, etc.

From Seismic to Reservoir Properties With Geostatistical Inversion

1999 ◽  
Vol 2 (04) ◽  
pp. 334-340 ◽  
Author(s):  
Philippe Lamy ◽  
P.A. Swaby ◽  
P.S. Rowbotham ◽  
Olivier Dubrule ◽  
A. Haas
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
A Priori ◽  
Three Dimensional ◽  
Well Logs ◽  
Inversion Method ◽  
Inversion Process ◽  
Reservoir Properties ◽  
Stratigraphic Model ◽  
Geostatistical Inversion

Summary The methodology presented in this paper incorporates seismic data, geological knowledge and well logs to produce models of reservoir parameters and uncertainties associated with them. A three-dimensional (3D) seismic dataset is inverted within a geological and stratigraphic model using the geostatistical inversion technique. Several reservoir-scale acoustic impedance blocks are obtained and quantification of uncertainty is determined by computing statistics on these 3D blocks. Combining these statistics with the kriging of the reservoir parameter well logs allows the transformation of impedances into reservoir parameters. This combination is similar to performing a collocated cokriging of the acoustic impedances. Introduction Our geostatistical inversion approach is used to invert seismic traces within a geological and stratigraphic model. At each seismic trace location, a large number of acoustic impedance (AI) traces are generated by conditional simulation, and a local objective function is minimized to find the trace that best fits the actual seismic trace. Several three-dimensional (3D) AI realizations are obtained, all of which are constrained by both the well logs and seismic data. Statistics are then computed in each stratigraphic cell of the 3D results to quantify the nonuniqueness of the solution and to summarize the information provided by individual realizations. Finally, AI are transformed into other reservoir parameters such as Vshale through a statistical petrophysical relationship. This transformation is used to map Vshale between wells, by combining information derived from Vshale logs with information derived from AI blocks. The final block(s) can then be mapped from the time to the depth domain and used for building the flow simulation models or for defining reservoir characterization maps (e.g., net to gross, hydrocarbon pore volume). We illustrate the geostatistical inversion method with results from an actual case study. The construction of the a-priori model in time, the inversion, and the final reservoir parameters in depth are described. These results show the benefit of a multidisciplinary approach, and illustrate how the geostatistical inversion method provides clear quantification of uncertainties affecting the modeling of reservoir properties between wells. Methodology The Geostatistical Inversion Approach. This methodology was introduced by Bortoli et al.1 and Haas and Dubrule.2 It is also discussed in Dubrule et al.3 and Rowbotham et al.4 Its application on a synthetic case is described in Dubrule et al.5 A brief review of the method will be presented here, emphasizing how seismic data and well logs are incorporated into the inversion process. The first step is to build a geological model of the reservoir in seismic time. Surfaces are derived from sets of picks defining the interpreted seismic. These surfaces are important sincethey delineate the main layers of the reservoir and, as we will see below, the statistical model associated with these layers, andthey control the 3D stratigraphic grid construction. The structure of this grid (onlap, eroded, or proportional) depends on the geological context. The maximum vertical discretization may be higher than that of the seismic, typically from 1 to 4 milliseconds. The horizontal discretization is equal to the number of seismic traces to invert in each direction (one trace per cell in map view). Raw AI logs at the wells have to be located within this stratigraphic grid since they will be used as conditioning data during the inversion process. It is essential that well logs should be properly calibrated with the seismic. This implies that a representative seismic wavelet has been matched to the wells, by comparing the convolved reflectivity well log response with the seismic response at the same location. This issue is described more fully in Rowbotham et al.4 Geostatistical parameters are determined by using both the wells and seismic data. Lateral variograms are computed from the seismic mapped into the stratigraphic grid. Well logs are used to both give an a priori model (AI mean and standard deviation) per stratum and to compute vertical variograms. The geostatistical inversion process can then be started. A random path is followed by the simulation procedure, and at each randomly drawn trace location AI trace values can be generated by sequential Gaussian simulation (SGS). A large number of AI traces are generated at the same location and the corresponding reflectivities are calculated. After convolution with the wavelet, the AI trace that leads to the best fit with the actual seismic is kept and merged with the wells and the previously simulated AI traces. The 3D block is therefore filled sequentially, trace after trace (see Fig. 1). It is possible to ignore the seismic data in the simulation process by generating only one trace at any (X, Y) location and automatically keeping it as "the best one." In this case, realizations are only constrained by the wells and the geostatistical model (a-priori parameters and variograms).

Acoustic impedance logs computed from seismic traces

Geophysics ◽  
1979 ◽  
Vol 44 (9) ◽  
pp. 1485-1501 ◽  
Author(s):  
M. Becquey ◽  
M. Lavergne ◽  
C. Willm
Keyword(s):  
Time Series ◽  
Acoustic Impedance ◽  
Seismic Velocity ◽  
Physical Property ◽  
Well Logs ◽  
Accurate Estimation ◽  
Low Frequencies ◽  
Short Period ◽  
And Migration ◽  
Initial Processing

Acoustic impedance, the product of seismic velocity and density, is a basic physical property of rocks. Seismic traces are converted into pseudoreflection‐coefficient time series by appropriate initial processing, then into acoustic impedance by the inversion of the time series. Such pseudologs are roughly equivalent to logs recorded in wells drilled at every seismic trace location. They yield important information concerning the nature of the rock and variations in lithology. To obtain the best quality pseudologs, careful initial processing is necessary: true‐amplitude recovery, appropriate deconvolution, common‐depth‐point (CDP) stack, wave‐shaping, wave‐equation migration, and amplitude scaling. The low frequencies from moveout velocity information are inserted. Both the short‐period information computed from reflection amplitudes and the long‐period trend computed from reflection moveout are displayed on acoustic impedance logs. Possible causes of pseudolog distortions are inaccuracies of amplitude recovery and scaling, imperfection of deconvolution and migration, and difficulties of calibrating the pseudolog to an acoustic log derived from well logs. Such calibration increases the precision; facies variations observed in well logs can be extrapoled to large distances from the wells, leading to a more accurate estimation of hydrocarbon reserves.

High-resolution acoustic impedance inversion to characterize turbidites at Marlim Field, Campos Basin, Brazil

2014 ◽  
Vol 2 (3) ◽  
pp. T143-T153 ◽  
Author(s):  
Tatiane M. Nascimento ◽  
Paulo T. L. Menezes ◽  
Igor L. Braga
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Structural Information ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Background Model ◽  
Rock Properties ◽  
High Porosity ◽  
Campos Basin

Seismic inversion is routinely used to determine rock properties, such as acoustic impedance and porosity, from seismic data. Nonuniqueness of the solutions is a major issue. A good strategy to reduce this inherent ambiguity of the inversion procedure is to introduce stratigraphic and structural information a priori to better construct the low-frequency background model. This is particularly relevant when studying heterogeneous deepwater turbidite reservoirs that form prolific, but complex, hydrocarbon plays in the Brazilian offshore basins. We evaluated a high-resolution inversion workflow applied to 3D seismic data at Marlim Field, Campos Basin, to recover acoustic impedance and porosity of the turbidites reservoirs. The Marlim sandstones consist of an Oligocene/Miocene deepwater turbidite system forming a series of amalgamated bodies. The main advantage of our workflow is to incorporate the interpreter’s knowledge about the local stratigraphy to construct an enhanced background model, and then extract a higher resolution image from the seismic data. High-porosity zones were associated to the reservoirs facies; meanwhile, the nonreservoir facies were identified as low-porosity zones.

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