Browsing by Author "Eladj, S."

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Analysis and interpretation of environment sequence models in Hassi R’Mel Field in Algeria
(2009) Baouche, Rafik; Nedjari, A.; Eladj, S.; Chaouchi, Rabah
Analysis of rock mechanical parameters from well log data and Dipole Shear sonic Imager. Application to Algerian sahara "Algeria"
(2009) Eladj, S.; Baouche, Rafik
The use of 'DSI' (Dipole Shear sonic Imager), in the Tin Fouye Tabankort area in Algeria allowed the exploitation of the rock mechanics properties in the field of drilling having for objective determination of the margin of ability of the well. The tool 'DSI' has a considerable advantage by its application which makes it possible to combine the technology of monopole and of dipole and to offer an effective method for the determination of the acoustic dynamic mechanical properties in - situ. The practical results of this study showed that: 1 - The phenomenon of BIOT is less when the medium is impermeable, 2- There is a significant effect of the petrophysic properties on the mechanical properties expressed by the effect of the coefficient of BIOT on the variation of the values of the density of mud. The beach of variation of the stability of the well obtained starting from the tool 'DSI' lies between the values 1.40 g/cc and 1.80 g/cc. On the other hand the results obtained by the application of Leake-off test and the successive increase in the density of mud vary from 1.50 g/cc with 1.90 g/cc. With the base of these results, it is necessary to note that the result obtained by tool DSI is almost closer than the practical methods and the percentage of error obtained by the application of this tool is due to the difference between the dynamic and static mechanical properties. The finality of this study is to determine a field of application of this new technique in the study of the stability of the well during drilling
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
Classification of ordovician tight reservoir facies in Algeria by using Neuro-Fuzzy algorithm
(Springer, 2022) Doghmane, Mohamed Zinelabidine; Ouadfeul, Sid-Ali; Benaissa, Z.; Eladj, S.
The Tight reservoirs in Algeria are generally characterized by their complex nature and their degree of heterogeneity. Wherein, the quantitative evaluation of such type of reservoirs necessitate the determination of facies in order to estimate the in-situ hydrocarbons and their nature. However, the classical methods of determining facies are essentially based on core data and carrots, which are not always technically available. Artificial neural network (ANN) is one of the recent developed methods being used to provide facies classification with a minimum available core data and by using well logs. Even though, the ANN results are acceptable, it determines only the dominant facies at each depth point off logs, no information can be provided for the secondary facies. For that reason, the main objective of this study is to develop a Neuro-fuzzy algorithm that allows the determination of secondary facies in addition to dominant facies. Indeed, the algorithm has been trained by using core data at wells’ scale in the Ordovician reservoir located in an Algerian southern Petroleum field. Moreover, the Neuro-fuzzy classifier has been tested in near wells, for which, the obtained results has demonstrated the effectiveness of the proposed approach to improve tight reservoir characterization in the studied field. Hence, the designed algorithm is highly recommended for other petroleum systems in Middle East and North Africa region
Design of optimal decentralized controller using overlapping decomposition for smart building system
(Springer, 2020) Doghmane, Mohamed Zinelabidine; Kidouche, Madjid; Eladj, S.; Belahcene, B.
Many industrial systems are known to have complex structure with large dimension variables. For such type of complexities, it is generally preferable to evade the design of centralized controller because of dimensionality augmentation in the step of implementation. Many research studies have been focused on designing decentralized controller for large scale systems. The aim of this paper is not just designing high dimension decentralized controller but also increase the robustness and improve systems’ performance, the optimality of these systems has been considered and discussed in the frame work of mathematical development of inclusion-contraction principle and overlapping decomposition. Furthermore, the proposed control strategy has been applied to a smart building system in order to minimize the damage caused by earthquake; the obtained results allow us to conclude that the proposed control strategy can be so useful for constructing smart cities
Identification and modeling of a rotary kiln in cement plant based on ANN (MLP)
(Springer, 2022) Doghmane, Mohamed Zinelabidine; Kidouche, Madjid; Eladj, S.; Ouali, A.
The objective of this study is to identify and model a rotary cement kiln based on production history data by using an artificial neural network MLP algorithm. The usefulness of this algorithm is that it provides a reliable empirical relation between the inputs parameters (Flow, Temperature, and pressure) and the outputs, which indicate the cement quality. Where, the most critical process in a cement production facility is cooking the mixed raw material in a rotary kiln; its task is to gradually burn and bakes a suitable mixture of input material to produce clinker. Therefore, the rotary kiln is the most important part in a cement factory. From another side, the control of a cement kiln is a complex process due to many factors namely: The Non linearity of the system caused by the chemical reactions, its dynamic and high dimensionality. Therefore, identification, modelling, prediction and simulation of Kiln system is very crucial step in managing and optimizing the cement production. Since the ANN has demonstrated its effectiveness in identifying a large class of complex nonlinear systems, it has been proposed in this case study to model cement Kiln of plant based on Multi-Layer Perceptron (MLP) approach. The MLP algorithm has been trained by using history data of twenty four months, and it has been tested and validated through comparison with production data of the next six months after the training. The obtained results have demonstrated the superiority of the proposed ANN approach over the conventional modelling approaches
Image-based processing techniques applied to seismic data filtering
(Elsevier, 2013) Ferahtia, J.; Aïfa, Tahar; Baddari, K.; Djarfour, Noureddine; Eladj, S.
Lithological Characterization by Simultaneous Seismic Inversion in Algerian South Eastern Field
(Engineering, Technology & Applied Science Research (ETASR), 2020) Eladj, S.; Lounissi, T. K.; Doghmane, Mohamed Zinelabidine; Djeddi, M.
The main goal of this paper is to characterize a reservoir situated in the southeast of Algeria based on AVO seismic inversion. The seismic inversion model has been built by the iterative method of Aki and Richards’s approximation and it has been correlated with four-existing wells in the studied zone. The correlation rate between the inversion model and logging data is good (varying from 72% to 85%). Reservoir characterization of this field has been given in detail. The lithological description is used to construct a Geomechanical model that is useful for new wells’ drilling decisions. The high correlated results allowed us to have a vision on the horizontal variation of Petrophysical parameters such as density and lithological variation of three facies clay, tight limestone, and porous limestone. Moreover, this classification is used in the best way to determine the interesting zone with higher porosity values, so that the exploration strategy becomes more efficient with minimized uncertainties. Therefore, it is highly recommended to use the constructed model to propose new wells as well-5 in this study
A sedimentological approach to refining reservoir architecture using the well log data and core analysis in the saharan platform of algeria
(2009) Baouche, Rafik; Nedjari, A.; Eladj, S.
Improved reservoir characterisation in the mature oil applied to Gourara Field of Sahara in Algeria, aimed at maximising both in-field and near-field hydrocarbon potential, requires a clearer understanding of sub-seismic stratigraphy and facies distributions. In this context, we present a regional, high-resolution sequence stratigraphic framework for the Oued Namouss Field based on extensive sedimentological re-interpretation of core and wireline log data, combined with core analysis and published literature. This framework is used to place individual reservoirs in an appropriate regional context, thus resulting in the identification of subtle sedimentological and tectono-stratigraphic features of reservoir architecture that have been previously overlooked. We emphasise the following insights gained from our regional, high-resolution sequence stratigraphic synthesis: (1) improved definition of temporal and spatial trends in deposition both within and between individual reservoirs, (2) development of regionally consistent, predictive sedimentological models for two enigmatic reservoir intervals (the Formations I and II), and (3) recognition of subtle local tectono-stratigraphic controls on reservoir architecture, and their links to the regional structural evolution of the Province. We discuss the potential applications of these insights to the identification of additional exploration potential and to improved ultimate recovery.In this research a procedure was developed to assess and quantify uncertainties in hydrocarbon estimates related to depositional facies, petrophysical data and gross reservoir volumes. This procedure was applied to the Gourara Field, which is a mature gas field in the Oued Namous Basin, Algeria. The aim was to investigate the reasons for an unexpectedly high hydrocarbon recovery factor
Segmentation and extraction of GBM tumor in brain MRI medical images : comparative study
(Springer, 2022) Doghmane, Mohamed Zinelabidine; Driss, M.; Eladj, S.
In this study, a comparison between two image segmentation methods has been discussed; the first method is based on normal brain's tissue recognition then tumor extraction using Thresholding method. The second method is classification based on fuzzy EM segmentation, which is used for both brain recognition and tumor extraction. Medical image examinations often use more information that are acquired from multiple imaging modalities, this use is increasing due to the complementary information that can be obtained from data fusion. The later improves the quality of the diagnosis; for instance, the image fusion can be developed for data from different modalities or different individuals, it may also concern fusion of image data with an external model, this can express prior knowledge about the problem at hand. The image data used in this comparative study belong to Algerian patients with Glioblastoma multiform. Since the goal of these methods is to detect, segment, extract, classify and measure properties of the brain normal and abnormal (tumor) tissues, the results of comparative study provided a guide tool of which method is more accurate in term of GBM volume estimation for the studied samples of the patients
Seismic inversion based on ANN: an advanced approach towards porosity model construction in the Algerian Saharan petroleum field
(2024) Eladj, S.; Doghmane, M.Z.; Benabid, M.K.; Aliouane, L.; Tee, K.F.; Nabawy, B.
Seismic inversion holds significant potential for providing crucial lithostratigraphic information in hydrocarbon reservoir characterisation and in identifying new traps. However, one of the major challenges in achieving reliable reservoir models in Algeria stems from the inherent uncertainties associated with seismic inversion algorithms and the non-linear relationship between petrophysical measurements. Due to their usefulness, several Artificial Neural Network algorithms have been developed and employed for seismic inversion and reservoir characterisation in the last few years. Nevertheless, only few researchers have addressed this issue in terms of optimisation of Multilayer FeedForward Neural Network (MLFN) architecture. In this case study, the use of an MLFN to address these challenges is proposed. The primary contribution of this research lies in the optimisation of the MLFN architecture based on trial and error procedures. The goal is to ensure that the computational demands are manageable within the constraints of available computing resources and that the process is time-efficient for geo-modellers. This practical approach is particularly valuable when applied at the reservoir scale. MLFN supervised training is conducted using logging data, where measured log curves serve as inputs, and core porosity, obtained from laboratory analysis, serves as target output. Moreover, coloured inversion is employed to generate a 3D seismic acoustic impedance cube, which, in turn, serves as input for a model-based inversion method designed to calculate porosity volume using the trained network. Furthermore, the usefulness of the resulting density cube is demonstrated through the correlation with density logs and core density values at wells 1, 2, and 3. Thence, the obtained correlation ranges validate the reliability of the obtained porosity volume in enhancing the characterisation of the targeted reservoir within the Algerian Saharan field.
Simulation protocol of water shut-off treatment for gas storage reservoir in Guantao Field (China)
(2023) Benallaou, S.; Eladj, S.; Doghmane, M.Z.; Keltoum Benabid, M.
Nowadays, oil and gas wells can produce excessive amounts of water in many fields. Excessive water production reduces oil and gas productivity by leading to fines and causing sand production and the corrosion of surface facilities. The combination of these unfavourable phenomena results in the early closure of wells as oil and/or gas production becomes unprofitable. In this study, a methodology for simulating water shut-off (WSO) treatments was tested on three synthetic reservoir models in order to conduct sensitivity studies on various parameters affecting the WSO treatment, such as polymer solution type, volume, and injection rate, for the purpose of evaluating different treatment scenarios, defining an optimal treatment design, and forecasting post-treatment well behaviour. Moreover, this protocol explains in detail the different stages of the reservoir simulation methodology for sensitivity analyses and describes the interpretation of WSO treatment results which enable the study of its application feasibility in a real field.
Structural boundaries delimitation from geomagnetic data using the continuous wavelet transform. Application to Hoggar (Algeria)
(Springer-Verlag, 2012) Ouadfeul, S.; Eladj, S.; Aliouane, Leila
The main goal of the proposed work is to delineate structural boundaries in a very complex geology environment using the spatial and statistical properties of the potential field data. The analysis is performed using magnetic anomaly of the total field data over In Ouzzal, an Archaean north–south elongated block belonging to the Hoggar (Algeria). This region is geologically and geophysically very poorly known except some localized areas. The intrinsic properties of high-frequency signals and the related causative sources are explored, thanks to two-dimensional continuous wavelet transform. The obtained results, represented by spatial distribution of the maxima of the modulus of the wavelet transform at each scale, clearly show that the major magnetic singularities of the field may be related to geological features. Comparison with the Euler’s deconvolution solutions exhibits a very good correlation. Even though where geological structures are known, our method shows better resolution and accuracy. The proposed multiscale method proves to be more powerful, easy to use, and versatile where classical methods of potential field interpretation fail or are very constraining. However, work is still ongoing to try to better and fully characterize the causative sources of the potential fields