... automated workflow algorithm application condition monitoring centrifugal compressor degradation operation compressors engines and turbines polytropic efficiency workflow dataset forecasting Introduction Every system undergoes degradation over time, requiring ongoing equipment maintenance...

... applications, including reservoir connectivity analysis, resource volume estimation, dynamic forecasting, well control optimization, flood optimization, and integration with surface network models. While RGNet has been proven effective, there is a vision for further enhancing its modeling precision...

... Abstract The majority of production forecasting methods currently used are point forecasting methods developed in the setting of individual well forecasting. For an actual oilfield, instead of needing to predict individual production time series, one is faced with forecasting thousands...

... Abstract One of the core assumptions of most deep learning-based data-driven models is that samples are independent. However, this assumption poses a key challenge in production forecasting - performance is influenced by well interference and reservoir connectivity. Most shale gas wells...

... Abstract Accurate well rate forecasting is essential for successful field development in the oil industry. Recurrent-based deep learning models have traditionally been used for production forecasting. However, recent advancements in the field have led to the use of transformer and transfer...

... of the proposed models are in good agreement with the ground truth data. Four Transformer-based predictive models demonstrate their applicability to forecast real-time drilling data of different lengths. Transformer models utilizing non-stationary attention exhibit superior prediction accuracy in the context...

... that are also stacked by interconnecting backcast and forecast links. The advantages of the framework are its flexibility with respect to different input parameters and various forecastable time series. This is particularly important for CPG to easily capture variations in the temporal dynamics and temperature...

... consists of a combined blend of analytical and machine learning models that allows to reliably forecast trends and proactively detect anomalies that may be negatively affecting the operational cash flow. Intuitive and portable visualizations allow a quick interpretation and communication of results among...

... real-time sensor data at the surface, historical performance, and sporadic test data. Additionally, forecasting models were developed for generating short-term (30 days) forecast of cumulative oil, water, gas, and liquid production, multiphase flow rates, WCT, GOR, and reservoir pressure. Using time...

... to develop and predict, understanding well performance has proven to be essential for driving value. Though there have been continuous advancements in well performance analysis and production forecasting for unconventional reservoirs that exhibit prolonged transient flow conditions, there is a still a gaping...

... Abstract The EVOLVE workflow addresses a longstanding and difficult problem in reservoir modeling: quantifying the uncertainty in NPV forecasts ensuing from uncertainties related to geological and simulation parameters, forecast scenarios, and economic variables. The EVOLVE workflow is linear...

... temperature grows with depth. To improve quality of the model initialization, every grid block received a value of the initial reservoir temperature derived from the block depth and temperature depth-gradient: Analogs are very difficult to use for steamflood forecasting because detailed information about...

... Abstract The difficulty in applying traditional reservoir simulation and modeling techniques for unconventional reservoir forecasting makes the use of statistical and modern machine learning techniques a relevant proposition for shale systems. However, the most current applications...

...Introduction Analysis and forecasting of production data in unconventional reservoirs continues to be problematic as there is considerable uncertainty related to our current lack of understanding of the relevant fluid flow phenomenon. There are many unknowns such as the link between flow...

... Abstract Determining the changes of production is the key aspect of forecasting the exploitation of unconventional reservoirs. Two-phase flow and flowback after fracturing is observed in that resources. To get to know production profile is needed to find the relationship between produced water...

... workflow for the uncertainty study and the methodologies used to overcome challenges in reservoir modeling and forecasting. The results of the sensitivity analysis and assisted history matching (AHM) process will be illustrated as well as how the results were applied in the evaluation of redevelopment...

... ultimate recovery and production forecast associated with the chosen development concept. A 2010 upstream industry benchmarking consortium reviewed global performance of E&P projects and concluded that the majority of production forecasts presented at project approval were not attained. In many cases...

... In recent years, probabilistic forecasting has gained popularity and has become the preferred approach when assessing the value of a project, given the uncertainty of many input variables. Uncertainties arise because both static and dynamic variables are ascertained from rather small volumetric...

... Abstract A production forecast is real enough, but making it is still 'real magic'. Why? Because when making it, we still have to quantify rocks we have not measured properly and assume, or even ignore a large number of future events (oil price, technology development, etc) which may later...

... thickness and the well performance used as an indicator of fracture intensity. Once the relation established, the neural network can be used to forecast primary production, or for mapping the reservoir fracture intensity. The resulting fracture intensity distribution can be used to represent the subsurface...