... production and operations, machine learning offers significant advantages in production forecasting and performance optimization. This study proposed a pioneering hybrid model integrating tabular, spatial, and temporal modalities to enhance production forecasting in unconventional shale gas reservoirs...

... accurately predict the remaining life of ESP and that the factors influencing ESP lifespan can be identified and quantified by the survival analysis model. machine learning artificial lift system survival probability probability artificial intelligence esp esp lifespan optimization survival...

... Summary This paper presents a realistic deepwater application of a deep-learning-based reservoir surrogate model in forecasting reservoir performance (via the prediction of state variables and well outputs) and nonlinearly constrained production optimization under geological uncertainty...

... flow metering oscillation presented artificial intelligence production control figure 6 measurement and control production monitoring well optimization clamp-on multiphase flowmeter unconventional field testing sft 3 figure 11 north sea flow measurement workshop reservoir...

... strategies and optimize operations. Data integration and contextualization, big data analytics and machine learning unlock deeper insights from vast datasets. Personalized offerings and targeted marketing through customer data analysis. Implementation of Generative AI solutions, like LLMs, SLMs, AI...

... Abstract The evaluation and optimization of wells with intelligent completion, whether multilayer or multilateral, requires a deep understanding of inflow characteristics at each inflow control valve (ICV). Zonal testing is crucial for gathering fluids and reservoir data; nonetheless, it leads...

... In this study, we look at the performance of our in-house TU-NLCPO optimizer for robust production optimization with the gradients of the objective function and the constraints computed using the StoSAG method. Specifically, we apply these optimization methods to the synthetic-realistic reservoir...

... Abstract This research addresses the challenges in using machine learning ( ML ) to assess and optimize production in unconventional wells, where computational costs (dependence on the accuracy of physical models) and complexity of wellbore design create significant challenges for decision...

... Abstract In the context of conceptualization and operation of refineries, achieving an optimal configuration emerges as a critical endeavor. Our proposed methodology transcends conventional approaches by seamlessly integrating domain expertise, empirical insights, and cutting-edge artificial...

... Abstract This paper addresses the challenge of optimizing subsurface hydrogen storage in porous media, a crucial component for advancing energy transition. The multifaceted nature of this challenge stems from the complex physics governing the process, coupled with operational limitations...

... Abstract In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature...

... with prior geological knowledge of subsurface reservoirs. deep learning machine learning geology artificial intelligence prediction forecast geologist reservoir simulation accuracy dnn-hm workflow ssim workflow noise optimization history matching cg-hm neural network iteration...

... Abstract We have developed the deep-learning-accelerated-gradient (DLAG) algorithm, a novel solution for well location optimization (WLO) problems that leverages data collected from the explored parameter space to accelerate optimization. During optimization, we collect data to train a deep...

... Abstract The industry has seen several methods of drilling optimization over the years. One of optimization's advantages—namely, its innate mathematical rigor—can also be seen as a disadvantage because of the downhole unknown and uncertain conditions. As a result, the traditional mathematical...

... asia government wellbore design completion equipment wellbore integrity plug envelope design for well integrity investigation scenario riglessly illustration leak packer workover completion brine optimization debris uae government packer leak below intervention bead adnoc onshore...

... of an inhibitive High-Performance Water Based Mud (HPWBM) that could be converted to a High Temperature-High Performances Water Based Mud (HT-HPWBM) while drilling, to cross the deeper and hotter sections of the well. This fluid was specifically engineered and optimized after each well in order to contain high...

... knowledge have several pitfalls: lack of generalization and explainability, data imbalance, and difficulty in optimization. This paper proposes a data-driven technique assisted by expert knowledge during the model building process, comprised of descriptive and predictive analytic frameworks...

... and their correlation with key market factors. By incorporating time-based optimization, the lags between market drivers and their impact on cost can also be understood. The models all showed significant predictability with R-squared values greater than 0.75. The authors find that this approach will help companies make...

... Abstract This study introduces a novel machine learning-based framework aimed at optimizing well placement for shale development. Traditional well-placement techniques, often reliant on physics-based modeling or empirical assumptions, have proven to be time-consuming and prone to suboptimal...

... CP ) k is the cycle lengths of the production well k . We consider a production optimization case where we fix the BHP of the production wells at each cycle. We also have a production optimization case where we fix production time fractions to 1 at each cycle of each production well so...