Publications Scientifiques

Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10

Browse

Author: search.filters.author.Hadjadj, AhmedSubject: search.filters.subject.Drilling

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Development of an expert-informed rig state classifier using naive bayes algorithm for invisible loss time measurement
    (Springer Nature, 2024) Youcefi, Mohamed Riad; Boukredera, Farouk Said; Ghalem, Khaled; Hadjadj, Ahmed; Ezenkwu, Chinedu Pascal
    The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts’ recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew’s efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT.
  • No Thumbnail Available
    Item
    New model for standpipe pressure prediction while drilling using group method of data handling
    (Elsevier, 2021) Youcefi, Mohamed Riad; Hadjadj, Ahmed; Boukredera, Farouk Said
    The continuous evaluation of the measured Stand Pipe Pressure (SPP) against a modeled SPP value in real-time involves the automatic detection of undesirable drilling events such as drill string washouts and mud pump failures. Numerous theoretical and experimental studies have been established to calculate the friction pressure losses using different rheological models and based on an extension of pipe flow correlations to an annular geometry. However, it would not be feasible to employ these models for real-time applications since they are limited to some conditions and intervals of application and require input parameters that might not be available in real-time on each rig. In this study, we applied the Group Method of Data Handling (GMDH) to develop a trustworthy model that can predict the SPP in real-time as a function of mud flow, well depth, RPM and the Fan VG viscometer reading at 600 and 300 rpm. In order to accomplish the modeling task, 3351 data points were collected from two wells from Algerian fields. Graphical and statistical assessment criteria disclosed that the model predictions are in excellent agreement with the experimental data with a coefficient of determination of 0.9666 and an average percent relative error less than 2.401%. Furthermore, another data (1594 data points) from well-3 was employed to validate the developed correlation for SPP. The obtained results confirmed that the proposed GMDH-SPP model can be applied in real-time to estimate the SPP with high accuracy. Besides, it was found that the proposed GMDH correlation follows the physically expected trends with respect to the employed input parameters. Lastly, the findings of this study can help for the early detection of downhole problems such as drill string washout, pump failure, and bit balling