RJPGT 2025

The paper presents a comprehensive study of the Pinda reservoirs in the Motoba field, Democratic Republic of Congo (DRC), highlighting the methodology adopted to collect and process geological and petrophysical data. The analysis focuses on the LP1, LP2, and LP3 layers, with the primary objective of locating suitable areas for the establishment of injector and producer wells. The analysis includes data collection and processing, followed by result interpretation to create maps of isobaths and iso-values of hydrocarbons in place. Tools such as Grapher and ArcGIS were used to visualize this information. The history of the Motoba field, discovered in 1975, is also discussed, illustrating the challenges and successes of production, particularly the water injection initiated in 1998 and resumed in 2011. The regional geology and stratigraphy of the Pinda formation are described, emphasizing the lithological complexity and diagenetic processes that have influenced reservoir quality. The study reveals that the Lower Pinda reservoir is divided into sub-units, each exhibiting distinct petrophysical characteristics. Data on porosity, saturation, and clay content were analyzed to establish a dynamic model for evaluating hydrocarbons in place. The results from the isobath maps show significant depth variation, identifying favorable areas for well implantations, particularly in the Northwest, East, and Southeast. Recovery factor analyses and GOR indicate opportunities for resource optimization, with regions identified as suitable for water injection. In conclusion, this paper underscores the importance of a methodical approach in reservoir evaluation and strategic planning of injection operations in the Motoba field.

n the era of advanced technology, the automation of industrial processes has become an essential element for improving efficiency and productivity. One of the most relevant aspects of this phenomenon is the development of robotic systems capable of performing repetitive tasks with a precision and speed superior to human ones. The objective of the work is the design and implementation of a robotic arm intended to perform the "pick and place" function, an essential process in numerous industrial fields, from product manufacturing to logistics and assembly. Pick and place involves picking an object from a specific location and placing it in another position. This task, while seemingly simple, is fundamental to the production flow of many industries. For example, in automobile manufacturing, components must be handled with care to ensure the quality and integrity of the final product. In logistics, robotic arms are used to organize goods in warehouses, reducing handling time and human error. The article presents the structure and operating specifications for the Doosan Robotics M1013 robot, as well as the RoboDK software, with its main features and application areas. After defining the specifications and selecting the hardware components, such as servo motors, sensors and control modules of the manufacturing cell, we move on to the design stage, where a 3D model of the robotic arm will be created. This model will allow the simulation of the robot's operation and the anticipation of possible problems, before the physical construction.

The structural approach to mechanisms from the perspective of component kinematic groups shows that as the class of component groups increases, the complexity of the mechanisms that contain them also increases. From this perspective, it is obvious that solving problems related to the kinematics of these mechanisms also becomes much more difficult to put into practice. In such cases, when mechanisms contain kinematic groups of a class greater than two, determining the analytical expressions of the parameters that establish the configuration of the mechanism during operation is extremely difficult. Therefore, in such cases, the development of some numerical calculation algorithms is indicated. The present paper analyzes the operation of a mechanism that has a triad as a component, in which one of the component kinematic joint is translational. To determine the variation of the position parameters, an iterative numerical calculation algorithm was developed and transposed into a computer program that allows the simulation of the functioning of the analyzed mechanism. Finally, the variation curves of these parameters over a kinematic cycle are presented, for a working configuration of the analyzed mechanism.

This research introduces a methodology employing Mini-Fall Off injection tests to enhance reservoir characterization and inform Drill Stem Testing (DST) decision-making in deepwater conventional reservoirs. The methodology begins with log-driven permeability estimation, which is corrected using mobility data from Wire Formation Tester (WFT) point results. Despite these corrections, uncertainties persisted regarding the dominant permeability, which is crucial for ensuring the minimum required deliverability for DST. To address these uncertainties and flow assurance challenges, the Mini-Fall Off injection test was employed to estimate permeability within the target flow zone unit of the conventional reservoir. This approach allowed for precise permeability assessment in specific zones, thereby enhancing the overall understanding of reservoir quality and well deliverability. The Mini-Fall Off test provided critical Value of Information (VOI) by revealing that the well deliverability did not meet the minimum required flow rates for DST, leading to the cancellation of DST and substantial cost savings. The application of Mini-Fall Off tests demonstrates a significant advancement in reservoir assessment strategies, offering a cost-effective and efficient alternative to traditional methods. By integrating log-driven permeability data with WFT-derived mobility data and employing the Mini-Fall Off test, the methodology provides a comprehensive framework for reducing uncertainties and optimizing resource allocation in the oil and gas industry. Furthermore, this technique underscores the importance of VOI in data acquisition and decision-making processes throughout the asset lifecycle, from exploration to development. The results of this study highlight the potential for Mini-Fall Off tests to become a standard practice in reservoir characterization, particularly in deepwater conventional reservoirs where cost and efficiency are paramount. This work presents a novel, cost-effective approach to obtaining accurate reservoir characterization, thereby demonstrating its potential to optimize operational efficiency and resource management in the oil and gas sector.

This research aims to identify issues with datasets that affect the training of machine learning models. The paper demonstrates the impact of feature processing on the model evaluation metrics. Initially, the paper evaluates unprocessed categorical features based on customized rules. The results of the seven evaluated algorithms show very low metric values. The demonstration is conducted on a dataset with 16 features for predicting depression among students. The dataset consists of 7,022 records. The issue of depression was chosen because it involves many features, which facilitate the analysis of the correlations between them. Moreover, the large number of features and records allowed for the analysis of generalization capacity by training on different dataset scenarios for all seven algorithms. The demonstration shows that data pre-processing generates better results when the inputs are exclusively numerical. Subsequently, the research demonstrates the importance of individually analyzing the contribution of each parameter within the model. The study encompasses three categories of tests, which are implemented using the ML.NET framework in the C# programming language.

The implementation of robotic system commands, along with the design of the component mechanisms, contributes to their correct functioning while performing various tasks. In this sense, the precise generation of interpolated movements at the level of the end effectors from the component of robotic mechanisms is particularly important in the optimal performance of tasks in many industrial applications. Compared to the point-to-point commands, the interpolated commands are intended to pilot the robotic mechanisms so that their final effectors follows an imposed trajectory in the operational space of the robot with an imposed hourly law. The paper presents a method for generating interpolated movements in the case of a robotic system whose active mechanism has an anthropomorphic structure. The presented method uses the differential model of the analyzed robotic system. The introduced interpolation is linear, and the hourly law used is in the form of a polynomial of the fifth degree. Within the simulations performed, the four possible configurations of the anthropomorphic robotic mechanism that allow the achievement of the imposed task were highlighted. Finally, for one of these configurations, the variation curves of the coordinates corresponding to the actuation motors for a certain task are established.

Previous studies carried out in the West-Congolian basin have highlighted potential formations that could play a major role in a petroleum system. In order to complete and enrich the geological and petroleum dossier of the said basin, a field trip to the Kitobola locality in the province of Central Congo was necessary to sample, analyze and describe the carbonate formations that outcrop there, in order to detect their importance from a petroleum point of view by describing and analyzing their mineralogical characteristics, and geochemical characteristics of these rocks, as well as evaluating their basic petrophysical parameters, notably porosity and permeability, in order to determine the role they might play in a petroleum system. Samples were sent to CRGM laboratories for petrographic, petrophysical and geochemical analyses, which revealed the presence of limestones (mudstones, wackestones and pelmicrite) with low porosity ranging from 1.18 to 1.49% and near-zero permeability between 0.2 and 0.5 mD. Geochemical analyses revealed a TOC ranging from 0.9 to 1.3 and high proportions of calcium oxide CaO associated with the limestones. As a result, these formations cannot be used as reservoirs, but rather as blankets, given their low porosity and near-zero permeability. On the other hand, they would make excellent source rocks in a petroleum system, given their very satisfactory TOC values. This article provides new information on the intrinsic characteristics (petrographic, petrophysical and geochemical) of the carbonate formations outcropping in the Kitobola region of the West Congolian Basin, while also highlighting potential source rocks or blankets.

Assessing the risk associated with drilling and wellbore stability studies requires the shear sonic log. These logs apart from distinguishing formation fluid from lithology are needed to obtain geo-mechanical rock parameters required for the safe design of rock fracturing. Although sonic logs are of great importance, they are usually not obtained due to the limiting cost of acquisition. Neural networks have been used to generate these logs to save cost, but these networks are prone to overfitting. The dropout rate has been proposed to tackle this problem, however selecting the optimum dropout probability rate can be challenging manually or expensive computation wise. This research therefore investigated concrete dropout, a dynamic technique for adapting the dropout rate of a neural network to the data. The concrete dropout technique was applied to an artificial neural network (ANN) and a convolutional neural network (CNN) model to predict the shear sonic log with Monte Carlo simulation. Comparison was also made with the deterministic ANN and CNN models which had no dropout layers added and a Bayesian-optimized multilayer perceptron (MLP) model. These models were trained and validated with four (4) wells from the Volve field, using features with the highest correlation. The Concrete dropout ANN was found to outperform both the deterministic versions and the MLP model with R2, RMSE, MSE and MAE scores of 0.9548, 3.6415, 2.4433 and 0.0179 respectively. The neural networks built in this study showed an enhanced predictive performance with concrete dropout addition over the networks with no dropout added, showing that the technique was able to adapt the dropout rate to fit the nature of data and improve performance, which finds great application in real time deployment. The findings of this study also proposed a cost-effective way of sampling and averaging multiple outputs from a single neural network model, leading to enhanced predictive performance as the addition of concrete dropout allowed the network output distributions rather than point predictions.

This work focuses on identifying potential areas in the Lokoro sub-basin, a strategic region located in the south-western part of the central Congolese basin. Covering an area of 51,759 km², this sub-basin encompasses the Mai-Ndombe province. Mapping the geological structures of this area is essential for highlighting hydrocarbon potential, a crucial aspect for future tenders. The study is based on the processing and analysis of magnetic data collected in the Lokoro sub-basin. The magnetic method, which evaluates the distortions of rocks buried in the earth's crust, enables the interpretation of magnetic anomalies linked to subsoil susceptibility. This approach provides clues to geological formations likely to accumulate hydrocarbons, offering valuable insight to prospectors. The anomalies detected may indicate the presence of geological structures favorable for hydrocarbon accumulation. By deepening our understanding of the geological structures of the Lokoro sub-basin, this research facilitates the exploration and exploitation of petroleum resources in this promising region. In sum, this study is a crucial step in revealing the hidden potential of the Lokoro sub-basin and in guiding petroleum exploration efforts in this resource-rich area.

Planning is a strategic approach that determines the orientations and objectives of a society in relation to the evolution of its territory. It serves as an appropriate tool for promoting sustainable development. In this context, the primary aim of this study was to design an exploration campaign in protected areas to minimize the potential risks that this initiative could pose to the well-being of local populations. Furthermore, it is crucial to establish new perspectives that could assist exploration engineers in conducting their various studies under normal and safe conditions. Through the techniques, methods, and tools applied during this study, we have discovered a central structure in our study area, which we consider a significant element that could certify the presence of a hydrocarbon deposit. Planning thus proves to be one of the most effective solutions for successfully carrying out an exploration campaign in protected areas, with the objective of minimizing the negative impact on the targeted site. By integrating appropriate management strategies and involving stakeholders, we can ensure a harmonious coexistence between exploration activities and environmental preservation.

The current era of digital technology has opened up endless possibilities for exploring and understanding our world. Among these advances, Geographic Information Systems (GIS) and remote sensing have revolutionized our ability to study and interpret geological phenomena without being physically present on the ground. The objective of this study therefore aims to use these tools to develop a more in-depth understanding of the geology of the Zagora province, a region of Morocco, using Landsat 9 images to produce a detailed geological map of the region characterized by a diversity of geological formations thus coupling the photo-interpretation approach and the Random Forest supervised classification algorithm. The results highlighted lithostratigraphic units, hydrographic networks and faults with great precision. The integration of these two methods provided complementary information, improving the accuracy and detail of the geological mapping of the area and highlights the importance of remote sensing tools and GIS for geological analysis and resource management.

Everyone is aware that access to or the search for drinking water is one of the major problems that bother all of humanity. The quarter of MOKALI, in the Kimbanseke township in the Democratic Republic of Congo, also faces challenges in ensuring an adequate supply of drinking water. Current infrastructures, designed several decades ago, are struggling to meet the growing demands of the population. This study focuses on improving the drinking water supply network by strengthening water production through the implementation of advanced drilling technics. Recent studies have highlighted the effectiveness of drilling as a method of accessing deeper aquifers, less prone to contamination and more sustainable over time [1]. In Mokali quarter, the integration of these techniques has not only improved water availability, but also reduced reliance on surface water sources, which are often polluted and unreliable [2],[3]. The use of rotary drilling technology, in particular, has enabled the extraction of high-quality groundwater from depths previously considered inaccessible [3]. The implementation of these technics was coupled with the modernization of the water distribution network, ensuring that the increase in production capacity directly translates into better access for the local population [3],[4]. The results of this study indicate a significant reduction in water shortage incidents and an overall improvement in public health outcomes in Mokali quarter. The success of this project suggests that similar strategies could be applied effectively in other regions facing comparable challenge.

An effective Occupational Health and Safety risk assessment is crucial for identifying potential hazards in the workplace and implementing measures to minimize harm. By evaluating risks, organizations can create a safer environment for employees, ensuring compliance with safety regulations and enhancing overall productivity. Regular assessments foster proactive safety culture and prevent accidents before they occur, ultimately promoting employee well-being and reducing organizational liabilities. This article aimed to analyze and assess the risks of injury and illness, identify the preventive and protective measures necessary to ensure a healthy and safe working environment, so as to highlight the importance of assessing the occupational risks associated with the jobs of chemical laboratory workers within the investigated company, focusing in particular on chromatography, sampling and waste water analysis activities. The results of the research facilitated the adoption of the most suitable measures to mitigate the risks highlighted as being located in the field of unacceptability. Through the careful and systematic examination of the activities carried out by chemical laboratories and the identification of potential hazards and related risks, practical and effective solutions to protect workers safety and health were already implemented.

Assessing and managing environmental risks in the oil industry is crucial to ensuring operational safety and corporate sustainability. In the case of Sep-Congo in Kinshasa, where petroleum product transport, storage and distribution activities are intensive, a proactive approach is required. Petroleum products present hazards such as toxicity, flammability and volatility, requiring rigorous risk identification. Dynamic risk assessment methods, such as DyPASI and DRA, enable potential accident scenarios to be updated in real time by incorporating data on past incidents and monitoring safety indicators. These approaches provide a visual representation of risks and help to make informed decisions to minimize hazards. By integrating these methodologies, Sep-Congo can strengthen its safety measures and ensure effective operation in the face of environmental challenges.

To minimize the risk of accidents and injuries, it is essential to assess how safety protocols are being adhered to in the workplace. Many attendants working at stations that refill cooking gas in the gas retail sector are found in this study location, Delta State of Nigeria. Such workers are exposed to numerous risks that can lead them to get injured, gas leakage, and fire. This study assessed the compliance of cooking gas refill station attendants in Effurun and its environs in Delta State with occupational safety practices. The study used cross-sectional and descriptive designs such as observations and interviews for qualitative data collection. The data was presented quantitatively as numerical values, from which statistical inferences were drawn. The study found that knowledge of safety practices among cooking gas refill station attendants in Effurun and its environs was generally good, especially regarding personal protective equipment (PPE) usage (85.4%), fire extinguishers (100%), and familiarity with workplace safety practices (95.8%). The study's results showed statistically significant associations and relationships between the respondents' age with awareness and knowledge (p=0.041) and duration of work time (p=0.018). Additionally, the general practice of safety measures was found to be high. However, some cooking gas refill stations had poor design and siting, which could increase the vulnerability to occupational accidents within the stations. To improve compliance, assessing occupational safety practices should be a top priority. This will positively influenced organizational improvement approaches and hazard management at cooking gas refilling stations in Nigeria.