... and evaluating the success of CO 2 sequestration. In scenarios where conventional simulations fall short, our model may offer a viable solution. petroleum play type geology co 2 subsurface storage reservoir characterization aquifer mechanism machine learning sequestration solubility efficiency...

..., and make prompt, informed decisions. geologist geology pressure transient testing reservoir surveillance machine learning flow metering well performance pressure transient analysis namazova identification muradov interpretation artificial intelligence production control application...

... core CT scans have been used to propagate the rock type in cored intervals. The resulting rock type curve was then used as a training data set for machine learning algorithms to populate rock types in un-cored intervals. Permeability thickness (kh) from well tests has been used to quality control...

... Abstract An open-source framework is presented for the development and evaluation of machine learning (ML) assisted Data-Driven models of carbon dioxide (CO 2 ) enhanced oil recovery (EOR) processes to predict oil production and CO 2 retention. This framework generated inputs and outputs...

... testing geologist reservoir simulation complex reservoir shale gas reservoir geomechanics rock type machine learning interpretation artificial intelligence evolutionary algorithm facies heterogeneity geological model directional drilling sedimentology modeling & simulation reservoir...

... machine learning geology reservoir navigation risk management geological subdiscipline geologist deep learning well planning reinforcement learning geosteering neural network constraint participant application interpretation gwc 2021 alyaev computational requirement artificial...

... not require fine-tuning or re-training a language model, it remains one of the most accessible ways to tailor LLMs to proprietary knowledge bases. deep learning information retrieval geologist artificial intelligence chatbot machine learning evaluation dataset large language model...

... production logging production monitoring reservoir simulation artificial intelligence machine learning drillstem testing hydrocarbon liquid reservoir surveillance case 1 linear regression mpm sm 3 production control allocation flowline simulated case 2 gor hypothesis information simulated...

... data, the capabilities and delivery quality of service vendors, the robustness of our machine learning models, the crucial importance of quality control, the significance of integrating petrophysical logs and PVT data, the pitfalls inherent in this technology, and provide general guidance for future...

... asgard exhibition midsand realization enhanced recovery machine learning physics-guided data-driven workflow application energy policy reservoir characterization moradi bypassed oil identification allocation technology Introduction In recent times, the probability of uncovering large...

... is a non-invasive machine-learning based approach. In this paper, we consider a physical twin consisting of a room-scale porous medium, instrumented with pressure sensors and photographic imaging. The digital twin is a reservoir simulator, however the precise choice of simulator is not material...

... of these areas will be addressed individually. communication protocol implementation drilling operation machine learning adoption information fusion specialist rig artificial intelligence recommendation drilling optimization interpretation prevention building trust successful adoption ai ml...

... reservoir permeability for injection ease. This study delves into the realm of data-driven decision-making, where the oil and gas industry is progressively harnessing the capabilities of Machine Learning (ML) and Deep Learning (DL) technologies. Specifically, we investigate the application of ML and DL...

... geological subdiscipline structural geology flow in porous media enhanced recovery machine learning subsurface storage artificial intelligence core analysis climate change sedimentary rock simulation pore geometry saturation rock typing aquifer reservoir simulation clastic rock rock type co...

... or years to hours or days. machine learning neural network artificial intelligence full ensemble upstream oil & gas modeling & simulation data conditioning reservoir characterization reservoir simulation network model rejection multiple stochastic approach multiple scenario...

... a conventional field trip. We illustrate our contribution using example VFTs designed for different audiences and geological topics. artificial intelligence geoscience concept health & medicine outcrop machine learning immunology buckley reservoir characterization upstream oil & gas...

... validation and ingestion, and how we manage, visualise, and interpret data, ensure full data integrity, and make data available for end-users through different applications. We will provide examples on how machine learning can be used for automated trend analysis and identification of relationships across...

... component. The user can specify the minimum length of a zone (e.g., 3 m), and the tool will merge and/or replace shorter zones based on the classifier's class probability distribution. The post-processing also assists the machine learning by removing impossible annular conditions from consideration...

... the well completion selection workflow. planning & scheduling machine learning upstream oil & gas completion iteration digital experience module artificial intelligence specification engineer requirement automation completion installation and operations procedure automated well...

... injection over 30 years. The compositional reservoir model is used to generate advanced mud data (light components C1 to C5) to predict fluid phase properties by machine learning. The predicted fluid properties are then compared to the actual model data. The data points represent different well locations...