... We built a deconvolution model for 1D induction log data in deviated wells using Machine Learning. Unlike iterative forward modeling inversion methods, the deconvolution model is extremely fast. Unlike linear deconvolution models in the past, the Machine Learning (ML)-based deconvolution finds...

... geophysical software into a cloud-based ML model development environment. Also, we describe its application with two examples, one predicting missing S-wave logs and the other improving seismic resolution on synthetic seismic data. Using this architecture, we can track the training data, and the model used...

... signals and ESP related noise were recorded by fiber optic cables installed inside the wellbore. In this paper, we compare DAS VSP data recorded with and without the ESP running and perform an analysis of the characteristics of flow noise activated by the ESP. We also use a machine learning (ML) algorithm...

... facies facies 0 society classification lamination applied geoscience resolution ML-based facies classi cation on acoustic image logs from Brazilian pre-salt region Nan You*, Yunyue Elita Li*, and Arthur Cheng *Sustainability Geophysics Project, Purdue University Sustainability Geophysics...

... Round-robin tests and laboratory audits have demonstrated that reservoir fluid compositions measurements can be systematically uncertain. To reduce compositional uncertainties, this work uses Machine Learning (ML) algorithms to update the existing measured fluid compositions and applies a compositional...

... Artificial Intelligence (AI) and Machine Learning (ML) classification models that were developed utilizing historical data to automatically validate conducted pressure and temperature measurement and communicate observations and alerts to engineers. The proposed method validates pressure and temperature...

... process. It does not only require significant labor time but also requires deep expertise in the production technology domain. The development team had three goals: 1) use open-source analytics, 2) develop a Machine Learning application (ML) to solve business challenges and finally 3) foster solutions...

.... AI/ML: Develop models to estimate missing data, such as production of the wells, field, network, and operational parameters, etc. In this paper we focus on improving the results of an existing solution by proposing the following actions: Abstract The oil industry is evolving...

... ML regressors were used for comparison and for consistency. We found the deconvolution method to invert the resistivity log accurately and fast. Furthermore, the bed boundaries were automatically inverted very accurately within 0.5 ft, the sampling interval of log data. For 2C40 induction log...

... porosity and permeability values to propagate prediction of textures, porosity and permeability along the well. The texture curves generated by the ML model are in good match with the recognized core section textures (CT Images). The results show that the NMR Total Porosity has a good correlation...

... features was prepared to digitize each rheology curve by incorporating the source water parameters, laboratory setup details, additive concentrations, and rheology (consistency and behavior indices) results. ML algorithms and techniques were then applied to the database to predict the rheology for given...

... Abstract See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using...

...A practical ML workflow to quantify dynamic reservoir property changes from 4D seismic Yang Zhang*, Lei Wei, Chevron Technical Center, Chevron; Shane Carley, and Raushan Kumar, North America Upstream, Chevron Summary water flood-front movement between injector-producer pairs and Oil-water contact...

... a meaningful interpretation of gas plume geometry. Time-lapse distributed acoustic sensing (DAS) has been proven recently as a viable technology for CO 2 monitoring; full-waveform inversion (FWI) and machine learning (ML) methods have become robust and practical tools in multiple applications in general...

... Abstract This paper presents how Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms in Exploration, Drilling Operations, Field Appraisal, Development and multiple 3D seismic volumes comparisons to minimize geological risk and uncertainty...

.... Gratitude Our gratitude to company "deeplight" for the provided "ML Oil" platform, as well as personally to Leonid Matyushin and Alexander Chapovsky (both – "deeplight") for significant assistance. This paper was selected for presentation by an SPE program committee following review...

... directly. We introduce the new method, we refer to as ML-adjoint, in the framework of Markov decision process (MDP). In MDP, a policy network takes input given by the predicted and measured data and outputs the adjoint source for back propagation in FWI. To achieve fast convergence in training, we...