With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm. For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently outperforms on model testing and prediction accuracy. A database was constructed for a heterogeneous reservoir. Four widely used boosting algorithms were used on the database to predict the design only from the output of a short injection/falloff test. Feature importance analysis was done on eight output parameters from the falloff analysis, and six were finalized for the model construction. The outputs selected for prediction were fracturing fluid efficiency, proppant mass, maximum proppant concentration, and injection rate. Extreme gradient boost (XGBoost), categorical boost (CatBoost), adaptive boost (AdaBoost), and light gradient boosting machine (LGBM) were the algorithms finalized for the comparative study. The sensitivity was done for a different number of classes (four, five, and six) to establish a balance between accuracy and prediction granularity. The results showed that the best algorithm choice was between XGBoost and CatBoost for the predicted parameters under certain model construction conditions. The accuracy for all outputs for the holdout sets varied between 80 and 92%, showing robust significance for a wider utilization of these models. Data science has contributed to various oil and gas industry domains and has tremendous applications in the stimulation domain. The research and review conducted in this paper add a valuable resource for the user to build digital databases and use the appropriate algorithm without much trial and error. Implementing this model reduced the complexity of the proppant fracturing treatment redesign process, enhanced operational efficiency, and reduced fracture damage by eliminating minifrac steps with crosslinked gel.

Vertical wells require diagnostic techniques after minifrac pumping to interpret fracture height growth. This interpretation provides vital input to hydraulic fracturing redesign workflows. The temperature log is the most widely used technique to determine fracture height through cooldown analysis. A data science approach is proposed to leverage available measurements, automate the interpretation process, and enhance operational efficiency while keeping confidence in the fracturing design. Data from 55 wells were ingested to establish proof of concept.The selected geomechanical rock texture parameters were based on the fracturing theory of net-pressure-controlled height growth. Interpreted fracture height from input temperature cooldown analysis was merged with the structured dataset. The dataset was constructed at a high vertical depth of resolution of 0.5 to 1 ft. Openhole log data such as gamma-ray and bulk density helped to characterize the rock type, and calculated mechanical properties from acoustic logs such as in-situ stress and Young's modulus characterize the fracture geometry development. Moreover, injection rate, volume, and net pressure during the calibration treatment affect the fracture height growth. A machine learning (ML) workflow was applied to multiple openhole log parameters, which were integrated with minifrac calibration parameters along with the varying depth of the reservoir. The 55 wells datasets with a cumulative 120,000 rows were divided into training and testing with a ratio of 80:20. A comparative algorithm study was conducted on the test set with nine algorithms, and CatBoost showed the best results with an RMSE of 4.13 followed by Random Forest with 4.25. CatBoost models utilize both categorical and numerical data. Stress, gamma-ray, and bulk density parameters affected the fracture height analyzed from the post-fracturing temperature logs. Following successful implementation in the pilot phase, the model can be extended to horizontal wells to validate predictions from commercial simulators where stress calculations were unreliable or where stress did not entirely reflect changes in rock type. By coupling the geometry measurement technology with data analysis, a useful automated model was successfully developed to enhance operational efficiency without compromising any part of the workflow. The advanced algorithm can be used in any field where precise fracture placement of a hydraulic fracture contributes directly to production potential. Also, the model can play a critical role in cube development to optimize lateral landing and lateral density for exploration fields.

Diagnostic pumping techniques are used routinely in proppant fracturing design. The pumping process can be time consuming; however, it yields technical confidence in treatment and productivity optimization. Recent developments in data analytics and machine learning can aid in shortening operational workflows and enhance project economics. Supervised learning was applied to an existing database to streamline the process and affect the design framework. Five classification algorithms were used for this study. The database was constructed through heterogeneous reservoir plays from the injection/falloff outputs. The algorithms used were support vector machine, decision tree, random forest, multinomial, and XGBoost. The number of classes was sensitized to establish a balance between model accuracy and prediction granularity. Fifteen cases were developed for a comprehensive comparison. A complete machine learning framework was constructed to work through each case set along with hyperparameter tuning to maximize accuracy. After the model was finalized, an extensive field validation workflow was deployed. The target outputs selected for the model were crosslinked fluid efficiency, total proppant mass, and maximum proppant concentration. The unsupervised clustering technique with t-SNE algorithm that was used first lacked accuracy. Supervised classification models showed better predictions. Cross-validation techniques showed an increasing trend of prediction accuracy. Feature selection was done using one-variable-at-a-time (OVAT) and a simple feature correlation study. Because the number of features and the dataset size were small, no features were eliminated from the final model building. Accuracy and F1 score calculations were used from the confusion matrix for evaluation, XGBoost showed excellent results with an accuracy of 74 to 95% for the output parameters. Fluid efficiency was categorized into three classes and yielded an accuracy of 96%. Proppant concentration and proppant mass predictions showed 77% and 86% accuracy, respectively, for the six-class case. The combination of high accuracy and fine granularity confirmed the potential application of machine learning models. The ratio of training to testing (holdout) across all cases ranged from 80:20 to 70:30. Model validations were done through an inverse problem of predicting and matching the fracture geometry and treatment pressures from the machine learning model design and the actual net pressure match. The simulations were conducted using advanced multiphysics simulations. The advantages of this innovative design approach showed four areas of improvement: reduction in polymer consumption by 30%, reduction of the flowback time by 25%, reduction of water usage by 30%, and enhanced operational efficiency by 60 to 65%.

Connecting the wellbore and reservoir rock systems through perforating is the primary mechanism to provide a flow path for hydrocarbons. In stimulation, this pathway becomes two dimensional (in functionality) because it is required to facilitate injection of fracturing fluids and production of reservoir fluids. Ineffective perforation can add of near-wellbore complexities. In this study, we looked at different perforation techniques from classical to recent contemporary. We investigated both stimulation and intervention aspects to provide pros and cons for these techniques and evaluate their effectiveness. Six challenging scenarios in stimulation were detailed with lessons learnt, best practices, and guidelines. These included deviated wells, soft rock formations, double pipe completions, fracture diversion requirement, horizontal wells with plug-and-perforate completions, and a mature asset. The workflows included perspectives such as perforating, fracture pressure analysis, and diagnostic injections. Efficient workflows for the well engineering cycle were also developed for the case when the injection rate cannot be established due to the inefficient wellbore−rock connection. Contingency interventions and bottomhole assembly (BHA) configurations were investigated with the goal of enabling a flexible strategy in a single intervention run to enhance injectivity. Currently, operational efficiency and business needs are paramount. This work presents integrated understanding, established practices, and resulting workflows to manage tradeoff and optimize the net present value of integrated projects.

Last year, the oil and gas industry was hit with one of the biggest challenges in the history of the industry — COVID-19. This has been a pandemic no one imagined would cripple the whole world and threaten our very existence. It caused an imbalance to the oil supply and demand where the global oil market had more than it could use or store, which drove oil prices to a record low. Despite this, projects in development have to be completed to accommodate the future rise in demand that is expected to occur after the pandemic. This paper will showcase how the rigless operations in Saudi Arabia in the oil and gas fields managed to continue with their activities toward tackling this challenge by capitalizing on two main principles. The first principle was business continuity management; preparing a strategic and operational framework to actively increase resilience to prevent suspension of the rigless operations or services, and thereby fulfilling the industry demand and preventing a cash flow interruption to the stakeholders. The second principle was the risk management process (RMP); identifying, monitoring, and managing all risks related to COVID-19, which minimized, and in some cases eliminated, its impact on the rigless operations. Both principles were the main pillars to first preserve lives and second to assure business continuity, which resulted in the continuation of the rigless operation and the profitability under the COVID-19 challenge for the company. COVID-19 was a risk no one accounted for. It was a true test to business continuity management and the risk management process. The integration of both processes in the oil industry, and specifically in rigless operations for the first time due to this pandemic, was essential to overcome the COVID-19 crises in addition to tailoring specific steps to address this challenge. Therefore, the rigless operations continued with planned activities while preserving people's lives.