Digital Integration Scope in Fracturing: Leveraging Domain Knowledge for Intelligent Advisors—Part I

Fracturing in horizontal wells influenced by high tectonic effects is challenging in terms of achieving rock breakdown and fracture propagation. Near-wellbore complexities also lead to insufficient injection rate, post-breakdown, to place proppant. A machine-learning (ML) model based on in-depth multidomain analysis can assist in such cases in the design and execution phase. Part I of the paper here covers the extensive ML modeling. The following Part II will cover the full implementation scheme applied on full well logs and complete data. A total of 106 fracturing stages were analyzed across 12 wells with a structured database created with 52 fracturing-relevant parameters. The dataset for ML modeling was skimmed down to 24 inputs and 4 output parameters. These included different phases of the well, such as drilling and completion, processed openhole logs, perforation details, fracturing treatment parameters, and pressure diagnostics data. A placement quality index (PQI) was calculated with mass of proppant placed, rate achieved, pressures experienced, etc. with application of appropriate weights on each. The PQI used weighting techniques such as the analytic hierarchy process and entropy weight method. Multiple classification and regression algorithms were tested and used to learn from these inputs to predict stage placement and proppant placement success. An algorithm comparison was done to select the best performing algorithms for each of the different prediction tasks. A detailed data exploration, feature engineering, and data preprocessing was conducted to study the correlations, establish causality, scale the data and prepare it to train/test the models. The proposed ML workflow in the study consists of a three-step process: (1) a classification model used to predict stage skipped, which is crucial as it influences the subsequent regression models. Results showed an excellent result in the predictions with an accuracy of 94%. (2) Multiple regression models were implemented to predict injectivity index, total proppant, proppant load, and the PQI. Predictions were evaluated using several evaluation metrics including R2 (varying from 0.86 to 0.93), root mean square error (RMSE), and mean absolute error (MAE). Results showed a good performance that varied across the different models. (3) A particle swarm optimizer algorithm was used downstream to optimize the perforation and treatment design to enhance the success ratio based on PQI prediction. The algorithm aimed to maximize the PQI by varying the parameters in the search space within reasonable and practicable ranges that was divided by completion type. Results showed an enhancement of 93% and 63% on low PQI section; 8% and 11% on mean values, for cased hole completion and for openhole completion, respectively. This work is a first attempt to use ML in enhancing proppant placement. This approach can be used with the existing reservoir quality, completion quality, and geologic quality indices to append the understanding and design of treatments and perforations. The deployment plan will be conducted into existing commercial numerical models to assist the engineers during the design process.

Placement Quality Index to Enhance Proppant Placement—Part I: The Machine-Learning Model

Almutary,

Khan,

Ugarte

et al. 2024

Mega Hydraulic Fracturing Operations with Innovative Efficiency Transformation

Alhosani,

Al Shehhi,

Refaei

et al. 2024

As United Arab Emirates (UAE) has untapped the unconventional resources, various studies have been implemented based on the uniqueness of resources and the operational challenges. As widely known, unconventional resources are closely related to operation efficiency to provide economical values to the shareholders. Earlier in 2024, a transformative plan to develop unconventional resources was launched. An unconventional resource transformative plan involves strategic development and utilization of non-traditional energy sources from carbonate source rocks to meet growing energy demands while reducing cost and environmental impact. This approach focuses on innovative hydrocarbon extraction technologies, such as multistage hydraulic fracturing on horizontal well, alongside sustainable practices that reduce carbon emissions. The plan aims to diversify energy portfolios, enhance energy security, and support economic growth by tapping into these abundant but technically challenging resources. By integrating advanced research, regulatory frameworks, and community engagement, the plan seeks to balance the benefits of unconventional resources with the need for environmental stewardship and social responsibility. Unconventional resource ramp-up plans outline a strategic approach to scale up operations to efficiently access these challenging reserves. This paper introduces transformative approaches to hydraulic fracturing in the UAE, emphasizing efficiency, cost reduction, and sustainability in unconventional resource operations. The efficiency project began with industry alignment, encompassing organizational behavior, drill-to-fracture well construction, operational practices, and the logistical environment. It extends to well engineering details that incorporate efficiency enablers. All these plans are currently being implemented in the project ramp up phase during various multi-year roadmaps, introducing a groundbreaking approach that successfully balances subsurface challenges with economic value. As these strategies are put into action, the project is already demonstrating enhanced efficiency in resource extraction and significant cost reductions. Economically, the project is achieving its goals of maximizing resource utilization. These early successes highlight the effectiveness of this new approach, setting a strong foundation for long-term sustainability and profitability in unconventional resource development.

Physics-Informed Machine Learning for Hydraulic Fracturing—Part II: The Transfer Learning Experiment

Khan,

Ugarte,

BinZiad

et al. 2024

The growth of machine learning (ML) approaches has sparked innovations in many applications, including hydraulic fracturing design. The crucial drawback in these models is the subjectivity and expertise of the design engineers, which could risk underrealizing the true reservoir and production potential. In Part I, a physics-based dataset was constructed using the physics of fracturing design theory and transformed into an ML model. Recent experiments aimed at testing this dataset with a transfer learning approach to enhance predictive capabilities in a real field dataset. The physics-based dataset is comprised of 62 parameters. During the application to the real dataset, it is crucial for the model to accurately predict and optimize the design using only a limited set of available parameters. The dataset was skimmed and tailored to the real data available, with domain expertise. Three training-testing dataset combinations were used for ML experiments: (a) synthetic-synthetic, (b) synthetic-real, and (c) real-real. The idea is to compare the three approaches to demonstrate the effectiveness and validity of transfer learning from a synthetic to a real dataset. Neural networks were utilized with multiple hyperparameter optimization routines. Additionally, a particle swarm optimization loop was integrated into the ML model to maximize production results. The dataset was reduced from 62 to 40 parameters based on domain understanding to tailor it to the real field dataset. A feed-forward multilayer perceptron (MLP) neural network was used for the ML modeling. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were the key evaluation metrics used. Out of the three ML experiments, the primary comparison was between the pure real data-trained approach and the transfer learning approach by adjusting the synthetically trained backbone with the real data. The five outputs were fluid efficiency, pad ratio, proppant mass, maximum proppant concentration, and dimensionless productivity index (JD). The transfer learning technique demonstrated enhanced performance across all five outputs, with an average RMSE improvement of 15.12% and an average MAPE improvement of 15.88% compared to the pure real data-trained approach. In the metaheuristic particle swarm optimizer, the parameter space was searched to maximize production. Multiple combinations of fluid efficiency, pad ratio, proppant mass, and concentration were varied within 10% of the initial prediction to maximize the objective function of JD. The optimized values were 14.2% higher on average compared to the initial prediction. Compared to the actual values, optimized values were optimal in 88% of the instances. The enhancement was even higher for lower initial JD values, where results were optimal 96% on average across models. Physics-based ML provides the advantage of intrinsic causality in the synthetic dataset. Transfer predictive learning opens an array of opportunities for small data utilization. The method bolsters full-scale deep-learning model creation in fracturing and in similar domains where limited records are available.