Juliana Finoto Bueno

Maximum Autocorrelation Factors applied to electrofacies classification

A vast amount of data is obtained during the development of a petroleum field. Seismic data, well logs, core and production data, all contribute to a better reservoir characterization and modeling. Several methods of multivariate data analysis can be used to support its interpretation, helping in important tasks as the identification of lithological facies. The most used and widely known of those methods is Principal Component Analysis (PCA) which intends to reduce data dimension while keeping as much as possible of their variance. Data dimension reduction can also be performed with the method of Maximum Autocorrelation Factors (MAF) which seeks to keep the spatial autocorrelation in data. In this work both methods were applied to data from well logs of the Namorado field, testing their performances in the classification of electrofacies. Following data dimension reduction, supervised classification methods known as k-nearest neighbors (kNN) and weighted k-nearest neighbors (wk-NN) were applied, and the results obtained were compared by cross-validation. MAF showed to be more efficient than PCA in reducing data dimension, while keeping relevant information. The wk-NN performed a little better in classifying electrofacies than the usual k-NN. According to these results, the combination of MAF and wk-NN can be a valuable tool for classifying the facies of uncored wells from their logs.

The Sergipano Belt

The Neoproterozoic Sergipano is the main orogenic belt between the Borborema province in the north and the Sao Francisco craton in the south. The belt is divisible from north to south into the Caninde, Poco Redondo-Maranco, Macurure, Vaza Barris, and Estância lithostratigraphic domains; the first three are composed of plutonic, volcanic and sedimentary rocks, and the last three of sedimentary rocks. Field relationships combined with zircon geochronology of igneous and sedimentary rocks and whole-rock elemental and isotopic data allow us to propose the following evolution for the Sergipano belt. An early Neoproterozoic (~980–960 Ma) continental arc (Poco Redondo tonalitic gneisses) developed on the margin of the Paleoproterozoic-Neoproterozoic Pernambuco-Alagoas block in the north. Extension of this continental block gave rise to: (i) A-type crustal melt granites and associated sedimentary rocks on the stretched, rifted margin of the Poco Redondo-Maranco domain; (ii) the Caninde rift sequence between the Pernambuco-Alagoas block and the Poco Redondo/Maranco domain; (iii) a passive margin on the southern boundary of the Pernambuco-Alagoas block, upon which sediments were deposited soon after 900 Ma; and (iv) a second sedimentary shelf on the passive margin of the Sao Francisco craton. In the Caninde domain, rifting continued until ca. 640 Ma and led to emplacement of a bimodal association of A-type granites (715 Ma) and continental mafic volcanic rocks, a continental-type layered gabbroic complex (ca. 700 Ma), magma-mingled gabbro/quartz–monzodiorite (688 Ma), and rapakivi granites (684 and 641 Ma). Deformed pillow basalts and interleaved marble lenses are likely ocean floor relicts in the Caninde domain. Closure of the Caninde oceanic basin began at ca. 630 Ma with the intrusion of arc-type granitic plutons in the Macurure (628–625 Ma), Caninde (ca. 621 Ma) and Poco Redondo-Maranco (ca. 625 Ma) domains. Convergence of the Pernambuco-Alagoas block and the Sao Francisco craton led to deformation on the passive margins and granite emplacement (590–570 Ma) mainly in the Macurure domain. Exhumation of the Pernambuco-Alagoas block and the Caninde, Poco Redondo-Maranco, and Macurure domains in the north led to deposition of uppermost clastic sediments in the Estância and Vaza Barris domains in the south, interpreted as a foreland basin, and to final thrusting of the continental margin sedimentary rocks onto the Sao Francisco craton.

3D acoustic impedance and porosity mapping from seismic inversion and neural networks

In this work we have applied a two-fold methodology to obtain porosity 3D maps from post-stack 3D seismic amplitude data and well log data. Firstly, acoustic impedance values are derived from seismic amplitudes by applying a L1-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain, where low-frequency impedance trends estimated at well-logs are used as constraints. Secondly, a feed-forward Neural Network (NN) is trained, validated and tested using effective porosity data observed at the well locations as input. The trained NN is applied for the whole reservoir volume to obtain a 3D effective porosity map. The results suggest that this simple workflow can be applied successfully specially in reservoirs for which the observed relationship between porosity and acoustic impedance is non-linear.