... water injection rate of each well and interval is critical for effective and successful water injection. This paper presents a fast and intelligent water injection allocation method based on comprehensive machine learning algorithm. The evaluation indexes of injection performance are established...

... to automatically detect stuck pipe accurately and early. Based on the in-depth theoretical analysis and historical stuck pipe data analysis, main early stuck pipe indicators during different drill operations are identified. More than ten time series analysis algorithms and machine learning algorithms are utilized...

... model with high interpretability was developed to evaluate the response of CO 2 -EOR processes using random forest (RF) to maximize performance. The feature importance and interaction analyses were conducted to enhance the interpretability of RF model from global to local levels. The genetic algorithm...

... reserves, and accurately forecasting production metrics. Meanwhile, in the interpretation of temperature profiles from DTS data, the flow-thermal coupled numerical models serve as the forward model while the inversion algorithms are utilized to obtain reservoir and production parameters. Unfortunately...

... crossing. This paper summarizes the current methods for the detection of kick event, the study and the findings from replay of historical kick alarm cases, the methods of calibrating pit volume during drilling operation, the algorithms for rig state detection, and the application of early kick detection...

... points incorporating data attributes such as completion data, cumulative production from the same reservoir zone, and reservoir properties. To ensure accuracy, the dataset is split into an 80:20 ratio for training and validation purposes. The Decision Tree machine learning algorithm is selected...

... is to predict synthetic caliper using logging while drilling (LWD) measurements (notably azimuthal density and petrophysical properties) with the help of artificial intelligence (AI) and machine learning algorithms that can be used in place of wireline caliper data thereby saving rig and logging costs and hence...

... permeability inflation coefficient development planning condensate reservoir ahm workflow localization algorithm Introduction The Ichthys gas-condensate field is located approximately 220 km offshore Western Australia in the Browse Basin, North West Shelf of Australia. Gas and condensate...

... innovations in business processes, application of design thinking methodology, agile development, and AI. The AI waterflood management solution combines cloud technologies, big data processing, data analytics, machine learning algorithms, robotics, sensors and monitoring system, automation, edge gateways...

...-consuming. This paper introduces an automated approach using computer vision and random forest algorithms to streamline the process, providing a more efficient method for noise reduction, multi-level thresholding, machine learning (ML) model training, and application to SEM images for quartz overgrowth...

... By leveraging these advanced techniques and technologies, the optimization and digitization of the conventional workflow are significantly enhanced. The use of ISA S75.01 methodology, API 14E guidelines, compressor simulation, and machine learning algorithms enables a more efficient and proactive...

... immensely. The benefits include minimization or reduction of costs associated with testing requirements, curtailed production, as well as the minimization of emissions. drillstem/well testing algorithm production control asia government reservoir surveillance well testing requirement drillstem...

... with a sampling algorithm called Markov Chain Monte Carlo (MCMC) that allows incorporating all the information from well logs, geological information, geostatistical parameters, and seismic data, generating models that honor the input data ( Hameed et al., 2011 ). Additionally, the method provides a solution...

... of the parameters is completed by minimizing the fitness function from the loss between proxy model and measured data with the help of optimization-based algorithm. The new method is validated against numerical simulation, covering a wide range of fluid properties and operation constraints. upstream oil...

... candidate oil wells will be carried out with this technology in a later phase of the project. This Artificial Intelligence based High GOR Well Control (acronym iGOR), technology encompasses the following. Design of a real time well GOR control system based on smart algorithm. The control solution...

...., identification of sweet spots). upstream oil & gas permeability flow in porous media reservoir characterization neural network machine learning prediction xgboost springbok sandstone queensland australia accuracy algorithm fluid dynamics artificial intelligence surat basin ann...

... update of validated weight data in Module 1, and eventually improve & enhance capability of both the EDA and Predictive module. Using a supervised machine learning algorithms, the identified target variables were observed to provide weight prediction between 16% to 38% of Mean Absolute Percentage...

... upstream oil & gas transmission sensor algorithm artificial intelligence wireless communication subsurface wireless communication interference communication smart mimo-ofdm wireless communication framework sensing-based reconstruction subsurface reservoir mimo-ofdm radar...

.... We postulated that different machine learning models would treat input variables differently, and with varying importance. Since we do not know apriori which variables are of greatest importance to the different ML algorithms, we needed a rapid prototyping approach, and we finally opted for the use...

... historical start-ups of LNG trains in Malaysia LNG. Having observed that the raw sensor data is bound to have noises and fluctuations due to instrumentation limitations, which may degrade the performance of machine learning algorithms, the data is aggregated on different time intervals to reduce...