HRS (Hampson Russell Software 1-CGG Veritas) was employed to perform time-depth conversions through seismic-well ties. P-wave velocity was calculated from sonic logs. We have used profiles of neutron porosity, bulk density, gamma ray and sonic travel time from well logs, and a 3D seismic data. We have interpreted the spatial distribution of the porosity values according to geological information obtained from the literature and from descriptions of core samples. This process provided a 3D numeric volume of porosity of the entire reservoir. In this chapter, we have predicted porosity values of a carbonate reservoir located on Campos Basin through an ANN method, applied to the integration of well log and 3D seismic data. These weights define the model that is used for prediction of unknown values. In the hidden layers, the neurons adjust the input data to the target well log values in the output layer, through an iterative calculation of weights. In the input layer, there are neurons that represent the input dataset. In general, an ANN is composed of an input layer, an output layer, and one or more intermediate layers that are hidden. They allow us to establish a quantitative relationship between the well log data and the seismic data, such that it can be used to predict a physical property in positions where there are no well log data. In this context, artificial neural networks (ANN) are tools to perform a multiattribute analysis. Multiattribute analysis employs a combination of various seismic attributes through mathematical modeling in order to increase the accuracy in the prediction of a particular property. Therefore, the application of multiattribute analysis has grown during the last few decades. Because each independent attribute provides a particular view of the seismic data, the use of a single attribute leads to a high uncertainty in interpretation. Depending on how it is derived, an attribute may help the interpreter to delineate geologic structures, map geologic features, estimate physical properties, etc. Ī seismic attribute is any direct or indirect information obtained from the seismic data through mathematical calculation and/or logical reasoning.
Integrated quantitative interpretation is used to estimate reservoir properties, obtained through seismic amplitudes and seismic attributes. In particular, the integration of well logs with seismic data is important in order to obtain some models with better vertical and horizontal resolutions, since well logs have a very restricted area and a better vertical resolution when compared to seismic data however, seismic data presents a better horizontal resolution and covers a larger area. For this reason, different types of data are used, such as geophysical well logs, and seismic, petrophysical, in addition to geological models, in order to predict reservoir properties such as porosity, lithology, and fluid saturation. Accurate characterization reduces the risk of drilling a dry well, as well as exploration and development costs. Reservoir characterization has become increasingly important to hydrocarbon exploration. Although isolated peaks of maximum porosity are observed, spatial patterns depicted in the model are associated with geological features such as different porosity types and cementation degree. Porosity values increase from southwest to the northeast portion, and lower values are found at depths related to the traced horizons.
The correlation coefficient and the error of the estimated values with respect to the actual values extracted along the wells are equal to 0.90 and 2.86%, respectively. In the second main stage, predictions of reservoir porosity values were obtained, as well as a 3D model, through the designed ANN. The estimated porosity values range from 5 to 30%. In the first main stage of the study, horizons were traced by following continuous seismic events on seismic sections, along depths between top and base of the reservoir.
The reservoir is composed of Albian carbonates. We have calculated and interpreted a 3D porosity model of a reservoir through the integration of 3D seismic data with geophysical well logs using an artificial neural network (ANN).
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