...URTeC: 4043196 Predicting Coiled-Tubing Drilling Dynamics Using Transformers Carlos Urdaneta*1, Cheolkyun Jeong1, Xuqing Wu2, Jiefu Chen2 1. SLB, 2. University of Houston. Copyright 2024, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2024-4043196 This paper was prepared...

... were identified and extracted. Prediction models for the four characterization parameters were established separately based on stacking ensemble learning algorithm. Specifically, the random forest (RF) and extreme gradient boosting (XGBoost) were introduced as the base models, and the linear regression...

...URTeC: 4034008 A Machine-Learning-Based Workflow for Drilling Risk Prediction of Wellbore Instability and Trajectory Optimization in Ultra-Deep Formation Hanqing Wang1, Ruxin Zhang2, Ziyan Deng3, Jin Meng1, Han Wang1, Yujie Zhou1, Fan Yang1, Ji Chang1, Yitian Xiao1, Huiwen Pang*4 1. SINOPEC...

... sedimentary rock clastic rock geology organic-rich rock geologist complex reservoir reservoir geomechanics prediction rpm fasken oil wolfcamp well logging reservoir characterization shale mudrock ikon science inc rock type barnett spraberry urtec unconventional resource...

... made for outlier samples. We then manually interpret key markers on sparse wells, which serve as the basis for training an DL model for marker propagation. This model predicts major markers on the remaining wells, and a marker QC step is implemented to further refine the results. The identified markers...

... fracture injection pressure clastic rock rock type reservoir geomechanics sindy scenario workflow sparse regression drillstem testing urtec model prediction application fracture fracture geometry URTeC: 4028108 Predicting Hydraulic Fracture Injection Pressure Using Hybrid Model...

... generation, model evaluation, and model inference. The ML system is applied here specifically to the creation of a basin model and then predicting well production on inventory locations. The modeling pipeline automates many of the steps needed for predicting well performance, enabling rapid experimentation...

... structural geology sandstone reservoir characterization urtec geologist rock type prediction geological subdiscipline sedimentary rock complex reservoir artificial intelligence machine learning algorithm porosity discriminator indirect prediction clastic rock shale gas...

...URTeC: 4043054 Casing Deformation Risk Prediction with Numerical Simulation During Hydraulic Fracturing in Deep Shale Gas Reservoirs Jianfa Wu1, Xuewen Shi1, Qiyong Gou1, Ersi Xu1, Dongjun Zhang1, Xiaoxu Ren2, Ting Yu2, Kehan Wu2, Yanming Tong2, Lipeng Wang2, Adrian Rodriguez-Herrera2 1. PetroChina...

...URTeC: 4044244 A Comparative Analysis of Machine Learning Techniques for Geothermal Wells Drilling Rate of Penetration (ROP) Prediction Taha Yehia*1, Moamen Gasser1, Hossam Ebaid1, Nathan Meehan1, Esuru Rita Okoroafor1 1. Harold Vance Department of Petroleum Engineering, Texas A&M University...

...URTeC: 4043980 A Novel Fusion: Integrating Artificial Neural Networks and Production Fit Models for Swift and Coherent Data-Physics Predictive Modeling of a Mature Oil Field in Texas Felipe Ospina!1, Pallav Sarma*1 Carlos Calad1, 1. Tachyus Copyright 2024, Unconventional Resources Technology...

... Abstract Shale gas reservoirs, such as Marcellus, Barnett, and Haynesville, play a pivotal role in meeting global energy demands. Accurate prediction of Estimated Ultimate Recovery (EUR) at an early stage is crucial for optimizing reservoir development strategies. Current methods face...

...URTeC: 4044039 Child Well Performance Predictions Samaneh Razzaghi*1, David Bonar1, Jerry Tkachyk1, Ashley Kalenchuk1, 1. Ovintiv Canada ULC. Copyright 2024, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2024-4044039 This paper was prepared for presentation...

... deep learning geologist complex reservoir reservoir surveillance neural network production monitoring machine learning production control dataset depletion function prediction forecast workflow geology artificial intelligence depletion urtec sensitivity accuracy validation...

...) assesses the induced seismicity risk of new SWD applications prior to issuing (or denying) a permit. It does so by analyzing variables that are thought to predict the likelihood of increased seismicity should that well become operational. In this study, we construct a dataset of 4616 Texas SWD wells...

... productivity through the utilization of machine learning algorithms. The proposed machine learning workflow generates predictive models using well completion parameters and chemical additives as input features, in addition to the initial three and twelve-months cumulative well production. The objective...

... conventional reservoirs have well-established techniques for production forecasting, unconventional reservoirs present unique challenges due to their complex geological characteristics. Accurate prediction and forecasting of production from this unconventional reservoir are crucial for optimizing field...

... fall short. Injected CO 2 in the subsurface reservoirs is commonly monitored with geophysical logs and seismic data. However, shear wave velocities are rarely acquired. We propose a Decision tree-based machine learning regression model to efficiently predict S-wave velocities from commonly available...

... geology complex reservoir well logging information machine learning drilling visualization log analysis interpretation geologist artificial intelligence real time system urtec modality anomaly engineer automated well-centric interpretation representation prediction drilling...

... eagle ford geologist deep learning artificial intelligence sedimentary rock machine learning complex reservoir ultimate recovery information geology reservoir surveillance nrmse production forecasting eur lstm unconventional reservoir eur prediction clastic rock rock type...