Centre for Quantum Information and Quantum Control (CQIQC)’s Post

📢 Read our Review paper 📚 Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study 🔗 https://lnkd.in/gJ8vqZbx 👨🔬 by Ms. Basmah Khalid Alotaibi et al. 🏫 King Fahd University of Petroleum & Minerals - KFUPM #federatedlearning

Uncertainty in reservoir characteristics can be challenging. Probabilistic models and sensitivity analysis are key to managing these uncertainties effectively

🛰️ Enhancing Streamflow Modeling by Integrating GRACE Data and Shared Socio-Economic Pathways (SSPs) with SWAT in Hongshui River Basin, China by Muhammad Touseef, et al. ➡️ https://brnw.ch/21wLslT

Paper from last semester on compositional reservoir simulation for PE 3022. Planning to start undergraduate research on the subject next semester.

Finally this article has been published together with my colleague Eder Castañeda. A work that reflects the contribution between two disciplines of great importance in reservoir characterization.

I am pleased to share our work presented at the Fourth EAGE Conference on Pre-Salt Reservoirs "Fracture Networks in Fault Zones: Insights from Core and Well Log Data of the Pre-Salt Reservoirs" ( https://lnkd.in/dCxp6ZMh). In this study, we used CT-scan data to analyze the topology of fractures in pre-salt carbonates, focusing on: 🔹 The interpretation of sub-seismic faults and the increased connectivity of fractures within the fault damage zone. 🔹 Calculating fracture intensity (P21) and comparing results across different data resolutions. Our findings highlight that fractures interpreted from Borehole Image (BHI) data are significantly underestimated compared to CT-scan results. This discrepancy underscores the importance of using high-resolution data for accurately characterizing fracture networks, particularly in complex reservoirs. This work demonstrates the value of integrating multi-scale data to interpret sub-seismic faults, assess fracture connectivity, and improve inputs for reservoir modeling.

I’m excited to share our latest paper titled ‘Analysis of facies proportions as a tool to quantify reservoir heterogeneity’, which has been published by Marine and Petroleum Geoscience https://lnkd.in/df5s43AR . We were able to quantify reservoir heterogeneity using a tool called ‘Panel Analyser’, developed by #SAFARI. We used a series of interpreted 3D outcrop models from three main depositional environments to demonstrate how heterogeneity can be quantified using Panel Analyser.  

Harnessing Machine Learning for Accurate Evaluation of Carbonate Reservoirs 📍This paper presents a comprehensive workflow integrating traditional petrophysical methods and machine learning to evaluate tight carbonate reservoirs. By using decision tree models, the study successfully identifies reservoir quality and fracture distribution, enhancing predictive capabilities for well performance and economic viability. 📍The machine learning approach clusters wells based on conventional log data, improving the classification of successful and unsuccessful wells. The decision tree model’s high precision and recall rates underscore its reliability, providing valuable insights for optimizing reservoir development strategies. https://lnkd.in/dK9shhNK #Carbonate #Reservoirs, #Machine #Learning, please#Petrophysical Analysis, #Well Clustering

Enhancing Carbonate Reservoir Evaluation with Machine Learning: A Game Changer for Well Performance and Economic Viability #AGPTnexus #Xegate #Startups #DarikNova #FutureForward #MachineLearning #ReservoirEngineering #CarbonateReservoirs #Petrophysics #OilAndGas #DataScience #EnergyInnovation #WellPerformance #DecisionTree #AI #DataDrivenInsights #ReservoirManagement #EconomicViability #Innovation #Tech

Enhancing Carbonate Reservoir Evaluation with Machine Learning: A Game Changer for Well Performance and Economic Viability #AGPTnexus  #Xegate  #Startups #DarikNova #FutureForward  #MachineLearning #ReservoirEngineering #CarbonateReservoirs #Petrophysics #OilAndGas #DataScience #EnergyInnovation #WellPerformance #DecisionTree #AI #DataDrivenInsights #ReservoirManagement #EconomicViability #Innovation #Tech