IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5558-d1515787.html
   My bibliography  Save this article

Downhole Camera Runs Validate the Capability of Machine Learning Models to Accurately Predict Perforation Entry Hole Diameter

Author

Listed:
  • Samuel Nashed

    (Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, The University of Oklahoma, Norman, OK 73019, USA)

  • Srijan Lnu

    (Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, The University of Oklahoma, Norman, OK 73019, USA)

  • Abdelali Guezei

    (Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, The University of Oklahoma, Norman, OK 73019, USA)

  • Oluchi Ejehu

    (Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, The University of Oklahoma, Norman, OK 73019, USA)

  • Rouzbeh Moghanloo

    (Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, The University of Oklahoma, Norman, OK 73019, USA)

Abstract

In the field of oil and gas well perforation, it is imperative to accurately forecast the casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry hole diameter enhances the design of both conventional and limited entry hydraulic fracturing, mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation, and reduces issues related to sand production. Implementing machine learning and deep learning models yields immediate and precise estimations of entry hole diameter, thereby facilitating the attainment of these objectives. The principal aim of this research is to develop sophisticated machine learning-based models proficient in predicting entry hole diameter under full downhole conditions. Ten machine learning and deep learning models have been developed utilizing readily available parameters routinely gathered during perforation operations, including perforation depth, rock density, shot phasing, shot density, fracture gradient, reservoir unconfined compressive strength, casing elastic limit, casing nominal weight, casing outer diameter, and gun diameter as input variables. These models are trained by utilizing actual casing entry hole diameter data acquired from deployed downhole cameras, which serve as the output for the X’ models. A comprehensive dataset from 53 wells has been utilized to meticulously develop and fine-tune various machine learning algorithms. These include Gradient Boosting, Linear Regression, Stochastic Gradient Descent, AdaBoost, Decision Trees, Random Forest, K-Nearest Neighbor, neural network, and Support Vector Machines. The results of the most effective machine learning models, specifically Gradient Boosting, Random Forest, AdaBoost, neural network (L-BFGS), and neural network (Adam), reveal exceptionally low values of mean absolute percent error (MAPE), root mean square error (RMSE), and mean squared error (MSE) in comparison to actual measurements of entry hole diameter. The recorded MAPE values are 4.6%, 4.4%, 4.7%, 4.9%, and 6.3%, with corresponding RMSE values of 0.057, 0.057, 0.058, 0.065, and 0.089, and MSE values of 0.003, 0.003, 0.003, 0.004, and 0.008, respectively. These low MAPE, RMSE, and MSE values verify the remarkably high accuracy of the generated models. This paper offers novel insights by demonstrating the improvements achieved in ongoing perforation operations through the application of a machine learning model for predicting entry hole diameter. The utilization of machine learning models presents a more accurate, expedient, real-time, and economically viable alternative to empirical models and deployed downhole cameras. Additionally, these machine learning models excel in accommodating a broad spectrum of guns, well completions, and reservoir parameters, a challenge that a singular empirical model struggled to address.

Suggested Citation

  • Samuel Nashed & Srijan Lnu & Abdelali Guezei & Oluchi Ejehu & Rouzbeh Moghanloo, 2024. "Downhole Camera Runs Validate the Capability of Machine Learning Models to Accurately Predict Perforation Entry Hole Diameter," Energies, MDPI, vol. 17(22), pages 1-20, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5558-:d:1515787
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5558/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/22/5558/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5558-:d:1515787. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.