... Abstract Decision making workflows in the energy industry require the capability to simulate hundreds if not thousands of models in a practical timeframe. In this paper we show how these capabilities can be achieved by exploiting top-class high performance computing resources available...

... Abstract Surfactant flooding is an enhanced oil recovery (EOR) technique that involves injecting surfactants into reservoirs to improve oil recovery. Pore network modelling (PNM) is a numerical method used to simulate fluid flow in porous media. While PNM has been successfully applied...

... (i) creation of uniformly spaced geometrical wells. These wells are created from user specified criterial for well spacing, orientation, length and offset from free water level (FWL). The created wells are run in simulation and subsequently ranked based on performance criterial like cumulative...

.... Estimate virtual production rate of wells. Configuration of Production Network Considered for Virtual Metering Abstract This paper describes effective use of a multi-phase simulator software to meter the production virtually and accurately from a group of wells that are produced through...

... Abstract Compositional reservoir simulation is a time bound activity demanding complex physics. We review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties, such as critical temperature and saturation pressure. A machine learning...

... Abstract This study focuses on developing a Deep Neural Network (DNN)-based reservoir simulator aimed at improving the efficiency and precision of hydraulic fracturing and production forecasting. Given the complex and nonlinear nature of reservoir behavior, our goal is to implement an automated...

... of biogenic methane at the top of the oil column whereas the base of the oil column shows solution gas methane that is 50% biogenic and 50% thermogenic. A proposed origin of this enigmatic oil column was recently proposed. Here, we explore this proposed origin with a compositional simulator. We simulated...

... with the increase of velocity. Heavy oil is a non-Newtonian fluid, but currently commercial reservoir numerical simulation simulators (ECLIPSE, CMG) do not have nonlinear numerical simulation function for heavy oil. In this paper, a new Hg Equation is introduced to match the nonlinear relationship between heavy oil...

... Abstract The objective of this work is to introduce the features of a new in-house geomechanics simulator. In this work, the capabilities of the new simulator will be demonstrated by applying it to coupled geomechanics and reservoir simulation models based on large-scale naturally fractured...

... simulator simulation Introduction Near-wellbore formation damage plays a major role in the final well production efficiency. Formation damage can take place at different stages of the well life, starting from drilling and completion phases and continuing to production/injection and workover phases...

... simulator for prediction and optimization using a case study of a horizontal well in Kuwait. The development plan for the targeted reservoir is to drill similar horizontal parallel wells, hence there is a valuable opportunity for rate of penetration optimization, due to the homogenous lithology...

... and materials reckmann society drillstring design stability technology conference kueck wob klbf high-frequency torsional oscillation bomidi simulator Introduction Application The Jonah Field in Western Wyoming contains prolific gas fields and offers many drilling challenges due...

... operation leakoff simulator scenario petroleum technology conference workflow Introduction Decades of hydraulic fracturing optimization for conventional reservoirs in mid-permeability reservoirs resulted in fair understanding of what fracture length and conductivity should be for a given...

... the injection of water at each time step. During inference, we apply Eq. (1) in an auto-regressive way to predict the long-term future, using the previous prediction as input, i.e. A standard learning objective for learned simulation is to minimize the Mean Squared Error (MSE) on the 1-step prediction...

... the fundamentals of effective modeling solutions that enabled multiple interdependent models to be integrated into a single production model while preserving the fidelity of individual models to reduce uncertainties and to increase confidence in the simulation results with consideration of various component model...

... to Scope 3 to benchmark our baselines so that we can put a plan to reduce them. This project established Carbon footprint as an Assessment tool to Simulate Emissions. The tool will provide a competitive advantage in terms of Sustainable Development for Schlumberger and other Oil & Gas industries...

... properties were reviewed and optimized. The optimization was performed using details obtained from proprietary simulators as well as by optimizing the slurry properties in the laboratory. The cement operation design called for a single slurry scheme with very critical parameters, ensuring the cement...

... of the reservoir simulator and tries to imitate a cycled injection regime, which results in a 7% higher NPV than the first case. enhanced recovery objective function reinforcement learning Upstream Oil & Gas machine learning neural network Artificial Intelligence simulator production optimization...

... This paper presents a physically consistent approach to modify black oil PVT tables to guarantees numerical consistency in reservoir simulation model stability with respect to phase and volumetric calculations. Three sets of checks are performed to detect the following data issues (1...

... the production models and propose an operative solution that maximizes the production. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data. The workflow starts with the modeling of the production...