... that account for core size, temperature, acid concentration, acid type, mineralogy, rock type and are independently scalable from linear to radial. Tardy et al. (2007) and Karale et al. (2016) have conducted both linear and radial to improve its prediction capabilities. They concluded that more acid...

... ( Ortiz et al. 2018 ). This variable is important to improve water saturation (SW) confidence from techniques based on resistivity. The following sections focus on evaluation of different analysis alternatives based on error for predictive models (linear and non- linear), that allow estimation...

... the sonic compressional velocity, gamma-ray reading and resistivity log at the same depth in the reservoir to confirm either interpretation. AI is used in a similar manner to uncover the relationships between all electrical logs, so that anomalies can be identified, and the correct log can be predicted...

... tracking capabilities. Meeting the net-zero emission paradigm will require a realignment of hydrocarbon production strategies with other forms of energy production, such as hydrogen and geothermal. Profiting from all these energy sources is only possible if accurate and timely predictions of the injection...

... a Cubic-Plus-Association (CPA) equation of state (EoS) using stochastics (Monte Carlo) and ML approach was carried out. To predict the asphaltene damage risk the asphaltene stability class index (ASCI) data and the in-situ live crude oil densities were used along with the support vector machines (SVM...

... Equation and Proper Orthogonal Decomposition model to represent the reservoir behavior; Long Short-Term Memory model to predict oil and water production rate; and an optimization component based on Iterative Local Search Metaheuristic, to search the near-optimal operational well constraints that maximize...

... Abstract A new mathematical model was proposed integrating the algorithms of neural networks and gradient boosting for the selection of the optimal EOR method in heavy and extra heavy oil fields as well as to predict the oil production of the field in the first five years of the project...

.... It was observed that the global PMOR could predict the overall trend in the pressure and saturation solutions at the well blocks but some bias was observed that resulted in discrepancies in prediction of quantities of interest (QoI) like oil production rates and water cut. Thus, the error correction model...

... Abstract The main objective of this project is to develop a model that, based on the data of previously drilled wells, allows the user to evaluate the mud plan and identify the probability of a stuck pipe event occurrence. This is done, by using statistical predictive models, particularly...

... with integrating the essential physical constraints in a typical waterflooding reservoir. Finally, we explore improvement on our LSTM model with EnKF. Problem Formulation Overview of Reservoir Simulation and Production Prediction Abstract Large data volumes and complex physical processes coupling...

..., filtering, model fitting and results presentation. It accounts for depletion driven changes and is able to accurately predict short and medium term evolutions in productivity. This would allow integrating the developed code with a global optimization model intended to be developed. The Multivariable...

... of water-oil relative permeability curves, measured over a single fractured Berea core by the unsteady state JBN method, with variable hydrostatic effective stress and capillary number are shown. The aim of the present study is to advance towards the prediction of complex dynamics in systems were matrix...

... better estimation and higher accuracy than the published empirical correlations. The present model provides predictions of the bubble pressure and formation volume factor with a correlation coefficient approximately of 98%. Trend tests were performed to check the behavior of the predicted values...

... Abstract The pore pressure prediction is the most important process in the design of drilling wells. This paper depicts a new methodology to analyze pore pressure based on both, the normal compaction theory of sediments and the way that normal behavior diverges when it is interrupted. Much has...

... Abstract Implementing a stochastic approach along with the conventional wellbore stability analysis enables to take into account uncertainty from input data in order to obtain more realistic and reliable predictions which improve decision making process in drilling operations. By means...

... in oil-water flow and compare it with model predictions. New experimental data on droplet size distribution are acquired for a gear pump with an in-line camera, utilizing the bead sizing tool. The acquired data capture the effects of the dispersed phase volumetric fraction and shear intensity. Two...

... velocity and slug frequency as well as pressure gradient and liquid holdup in the pipe. For these reasons, it is necessary to carry out experiments that allow a better understanding of slug flow pattern and improving mathematical models to predict slug characteristics. In this work, an experimental study...

... cost of coiled tubing intervention to perform liquid unloading. We discuss several aspects of job design and simulation. Predictive simulations based on API RP 19B Section 4 data and rock perforating models for sandstones indicated that perforating damage clean-up with dynamic underbalance would...

... × ( ρ L σ × g ) 1 4 Abstract The prediction of pressure drop in multiphase flow for risers is of particular interest for the oil industry and also a critical variable for the right design of surface facilities in offshore fields. Empirical steady state correlations...

... validating the rock / fluid properties. Among the different methods studied is the CO 2 injection, the Alternating Steam Injection more Gas (IAV + GAS), SAGP y VAPEX. enhanced recovery iav gas prediction SAGP cumulative production sensitivity optimum gas injection rate Upstream Oil & Gas...