... production recovery mitigations. The frac hit detection system resides in the cloud and runs a time-series machine learning (ML) model and knowledge-based expert system daily to detect frac hits on gas lift wells using well measurement data. The ML model relies on downhole and surface well measurements...

.... The approach makes use of such simplistic techniques as Machine-Learning, combined with conventional assessment and presentation approaches. Using this process, the methodology is able to readily identify any apparent and obvious statistical relationships between the well construction and casing deformation...

... the accuracy and generalizability of various machine learning models. The study explored three key areas: the development of a physics-informed multivariate regression (MVR) model, the integration of a hybrid, fully-coupled reservoir and fracture simulator with machine learning, and the incorporation...

... artificial intelligence well logging communication machine learning strategic planning and management fracture-driven interaction benchmarking proppant communication geologic feature parent well technology conference recovery asset and portfolio management fracturing materials knowledge...

... intelligence geological subdiscipline machine learning geologist reservoir simulation reservoir characterization reservoir pressure reservoir pressure profile fracture height fracture fracture geometry unconventional resource technology conference presented cumulative production variability...

... Abstract This paper demonstrates how the integration of multi-physics downhole imaging with machine learning techniques provides a step-change in perforation erosion analysis. We present a novel approach that improves measurement accuracy, consistency, and turnaround time. We reveal how...

... to legacy data acquired from the asset. After thoroughly evaluating the acquired data, a complete pad is history-matched using an integrated hydraulic fracturing and reservoir simulation workflow. The calibrated model is then used for a sensitivity study, which is further enhanced through machine learning...

... intelligence fracturing fluid sand concentration proppant fracturing materials geologist hydraulic fracturing geomechanics acoustic signal experiment frequency band machine learning perforation energy summation spectrum das measurement geological subdiscipline amplitude injection slurry...

... for analyzing perfs and the integration of machine learning models, comprehensive volumetric growth analysis is now achievable, completing the evolutionary journey ( Fig. 1 ). Figure 1 The evolutionary journey of perf imaging using high-resolution acoustic imaging technology. For clarity, P3D stands...

... identify any fracture approaching in real-time, with high confidence. The operator and service company trialed a new machine learning detection system parallel to traditional analysts to identify fractures in real-time and quickly relay updates to the operations team to implement this requirement...

... machine learning viscosity technology conference reduction gpt polymer loading artificial intelligence deep learning large language model chatbot Introduction Rheology Testing. Viscosity was measured at various shear rates between 1.0s -1 to 170s -1 . Also, viscosity readings were...

... Abstract The purpose of this work was to evaluate if frac water hammer signature characteristics are representative of important hydraulic fracture and reservoir properties in a horizontal well and if those characteristics can be used as inputs in a predictive Machine Learning model. Water...

..., providing some indication of the quality of the results. geologist fracture upstream oil & gas complex reservoir geology hydraulic fracturing machine learning algorithm discrete fracture network model spatial united states government north america government outlier stage 2 dbscan...

... productivity spe hydraulic fracturing technology conference calculation oil shale shale oil machine learning metric technology conference completion hhp-hr transition texas business-centric approach Despite these advances, there has not been a standard metric for assessing a frac crew’s...

... of similar vertical or lateral drainage, we have further identified compounds that showed higher sensitivity to vertical change and those that are more sensitive to lateral variation. This is realized by a machine-learning process leveraging geochemical data from 2,200+ wells across various basins...

...Methods In this work the authors use an in-house code based on Displacement Discontinuity Method (DDM) to generate all necessary data to feed a machine learning algorithm. For each specific fracturing scenario (single or multiple hydraulic fractures), the DDM software is used simulating...

... with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore while actively fracturing...

... machine learning hydraulic fracturing mesh sand friction reducer perforation proppant placement test series 2 hole size perforation erosion completion installation and operations upstream oil & gas erosion rate erosion petroleum engineer tank spe hydraulic fracturing technology...

... recognition and classification can be used to identify key parts of a fracturing job; demonstrating how this can be integrated into a cloud-based platform and used to speed up analysis. They also describe how machine learning techniques combat challenges of real time data, like automatic detection...

... intelligence drilling operation directional drilling reservoir characterization reservoir surveillance complex reservoir machine learning data quality hydraulic fracturing metric well spacing child completion delaware basin unconventional resource economics basin child well change boxplot...