Publications Scientifiques

Now showing 1 - 3 of 3

Study of Pore Level Influences on Reservoir Quality Based on Rock Typing: Case Study of Quartzite El Hamra, Algeria
(Springer Nature, 2024) Nettari, Ferhat; Doghmane, Mohamed Zinelabidine; Aliouane, Leila; Ouadfeul, Sid-Ali
One of the biggest challenges facing Geoscientists and reservoir modelers is how to improve the descriptive understanding of the hydrocarbons reservoir, and therefore, define the best representative reservoir properties (e.g., fluid flow capacity) in the simulation model, whereas poorly described reservoir characteristics can lead to a significant impact on reservoir performance predictions and its future production behaviors. In order to master the Quartzite El Hamra reservoir in Southern part of Hassi Messaoud field in Algeria, this study was dedicated to characterize the petrophysical properties by using rock typing and flow unit techniques (Winland R35 and FZI). The main objective was to evaluate the pore level’s influences on reservoir quality and log response and to study the relationships between the composition of pore geometry and reservoir quality. This allowed us to understand the factors that control the quality of the reservoir and the fluids’ flow characteristics. Moreover, this study was based on detailed description and laboratory tests on cores and thin sections and the integration of this information with geological, Petrophysical, and engineering data. Furthermore, appropriate set of reservoir properties (i.e., porosity—permeability ratio, R35, storage percentage, and percent flow) are well defined for six identified Hydraulic Flow Units (HFUs). The obtained results can improve reservoir simulation studies for performance prediction, history matching, and future development decisions in the field.
Application of local optimisation with Steepest Ascent Algorithm for the residual static corrections in a southern Algeria geophysical survey
(2022) Bansir, Fateh; Eladj, S.; Harrouchi, Lakhdar; Doghmane, M. Z.; Aliouane, Leila
Static corrections in the seismic data processing sequence are one of the most sensitive steps in seismic exploration undertaken in areas with complex topography and geology. Using the stack energy as an objective function for the inversion problem, static corrections can be performed without using the cross-correlation of all traces of a Common Depth Point (CDP) with all other CDP traces. This step is a time-consuming operation and requires huge computer memory capacities. The pre-calculation step of the crosscorrelation table can provide greater processing efficiency in practice; by either a local optimisation algorithm such as Steepest Ascent applied to the traces, or a global search method such as genetic algorithms. The sudden change of the topography and the signal/ noise (S/N) ratio decrease can cause failure in residual static (RS) corrections operations; consequently, it may lead to poor quality of the seismic section. In this study, we firstly created a synthetic seismic section (synthetic stack), which describes a geological model. Then, the Steepest Ascent Algorithm (SAA) method is used to estimate RS corrections and evaluate its performance, in order that the encountered problems in the field will be overcome. The generated synthetic stack, with a two-layer tabular geological model, has been disturbed by introducing wrong static corrections and random noise. Thus, the model became a noised stack with low S/N ratio and poor synthetic horizons continuity. After 110 iterations, the SAA estimated the appropriate corrections and eliminated disturbances introduced earlier. Moreover, it improves the quality of the stack and the continuity of synthetic horizons. Therefore, we have applied this algorithm using the same methodology for calculating the RS corrections of real data of seismic prospection in southern Algeria; the input data has poor quality caused by near-surface anomalies. We found that our proposed methodology has improved the RS corrections in comparison to currently used conventional methods in the seismic processing in Algerian industr
Levenberg-Marquardt algorithm neural network for clay volume estimation from well-log data in an unconventional tight sand gas reservoir of Ahnet basin (Algerian Sahara)
(Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, 2022) Aliouane, Leila
The main goal of this paper is to show the contribution of artificial intelligence, namely a neural network, in reservoir characterisation to predict the clay volume in an unconventional tight sand gas reservoir. Clay volume is usually estimated using the natural gamma ray log, which can give bad results if non-clayey radioactive minerals are present in the reservoir. Our purpose is to implement a multilayer perceptron neural network machine to predict the clay volume using the conventional well-log data as an input and the measured mineralogical component, as desired output with a Levenberg-Marquardt algorithm. Application to two Ordovician reservoir intervals of a borehole located in the Ahnet basin in the Algerian Sahara shows the contribution and the efficacy of the implemented neural network machine in unconventional tight sand reservoirs characterisation

Publications Scientifiques

Browse

Filters

Settings

Sort By

Results per page

Search Results