... curve algorithm Introduction Polymer flooding is a widely used technique in chemical enhanced oil recovery (CEOR) that has demonstrated substantial potential in improving oil recovery factors ( Sorbie 1991 , Sheng 2010 , Thomas 2019 , R.S. Seright 2023 ). By injecting polymers...

.... The pressure tests in the ambiguous zone are added to the quality check list. In the automatic classification phase, a machine learning algorithm based on K-Nearest Neighbour (KNN) and Light Grading Boosting Machine (LGBM) is applied to perform automatic classification. The pressure tests on which...

... For large datasets, determining the optimal number of clusters is complex. The algorithm's reliance on the Euclidean distance metric can also restrict its ability to detect clusters of varying shapes and identify overlapping clusters, impacting robustness in complex scenarios. The formula...

... and entropy weight method. Multiple classification and regression algorithms were tested and used to learn from these inputs to predict stage placement and proppant placement success. An algorithm comparison was done to select the best performing algorithms for each of the different prediction tasks...

... into categories. The approach consists of a fuzzy logic algorithm to categorize acquired surface data and a real-time classifier to be trained. The score calculated by the fuzzy algorithm is mapped into five categories, which serve as labels for training validation and test data. A Long Short-Term Memory (LSTM...

... estimation of each well thanks to Machine Learning and the all the historical data available (including well tests). Once a VFM model is built and validated by the user, it can be used to monitor the well and obtain a continuous estimated production rate. The TDASH tool uses Machine Learning algorithms...

... knowledge-assisted case-based reasoning algorithm reduce false alarm drilling operation early kick and loss detection interference artificial intelligence detection ekld system drilling fluid Introduction Early Kick/Loss Detection In the oil and gas industry, the early detection of kicks...

...-up tables, might lead to considerable financial and technical risk and uncertainty. This study discusses this problem and its drawbacks and further employs machine learning (ML) algorithms to develop comprehensive EOR screening guidelines for sandstone and carbonate reservoirs. This work applied ML...

... clastic rock geologist sedimentary rock multistage fracturing complex reservoir deep learning geology rock type mudrock artificial intelligence algorithm machine learning characteristic productivity prediction model test production neural network optimization algorithm hydraulic...

... to reduce the investment on costly and scarce lift gas and maximize oil recovery. In this work, an attempt is made to optimize a group of wells with application of an innovative metaheuristic algorithm. The objective function can be either the generated profit or total produced oil as a function of injected...

... management office of a large oil and gas operating company. machine learning precision score drone pile application waste disposal management algorithm classification detection dashboard drone solution confusion matrix detection model confidence level threshold artificial intelligence...

... balance. The workflow consists of the following steps: Production injection and pressure data preparation and cleansing. Wells clustering and material balance calculations using optimization algorithm to match surveillance data. Pressure interpolation in time using ML methods including...

... intelligence oil production icd configuration icd aicd configuration coefficient bayesian hyperparameter simulation permeability contrast algorithm cumulative oil production optimization workflow dynamic simulation Introduction ICD technologies, such as nozzle-based inflow control device...

... Abstract Intermittent mode is one of the most efficient methods of managing low-rate ESP wells. However, this approach implies defining optimal working parameters of the wells. So, there is a demand for complex algorithms that can perform simultaneous joint optimization of power consumption...

... previously conducted downhole corrosion classification data. Secondly, an advanced pool of ML algorithms was created, and trained on fetched corrosion data. Thirdly, the ML pool evaluated and tested to be uploaded into the system. Finally, newly acquired data for unlogged or old log wells are fed...

... translated in-house knowledge and operational best-practice from the GoT/WFS model into mathematical equations, integrating them into our optimized and automated workflow. The core algorithm leverages Integer Linear Programming (ILP) to incorporate geological features and drilling criteria, to eventually...

... one is the inversion algorithms developed so far lack robustness and efficiency when facing an extensive set of parameters and computationally expensive forward models. The other one is that existing inversion techniques for distributed fiber optic monitoring data rely solely on either DTS or DAS data...

..., and optimize drilling. System instability reduces drilling performance and the lifespan of surface and downhole tools. Service company and operator subject matter experts worked together to develop a method to detect instability. The resulting algorithm is used to monitor all the parameters affected...

.... Case Studies: Detailed exploration of unique algorithms and logics tailored for specific fields. Our strategy, executed in collaboration with seasoned Sand Management professionals and advanced tools, delves into rock mechanics at a granular level and enables the assessment of well-specific sand...

..." algorithm, prediction of properties using a graph neural network with selection of the best candidates, and laboratory synthesis and validation of the predicted properties. The Virtual Factory algorithm used 159 of the most widely produced precursors on the local market and 10 templates of most common...