Influence of Indefinite Causal Order on Otto Heat Engine

Quantum effect plays important roles in quantum thermodynamics, and recently the application of indefinite causal order to quantum thermodynamics has attracted much attentions. Based on two trapped ions, we propose a scheme to add an indefinite causal order to the isochoric cooling stroke of Otto engine through reservoir engineering. Then, we observe that the quasi-static efficiency of this heat engine is far beyond the efficiency of a normal Otto heat engine and may reach 1. When the power is its maximum, the efficiency is also much higher than that of a normal Otto heat engine. This enhancement may origin from the non-equilibrium of reservoir and the measurement on control qubit.

Hybrid Data Driven Approach for Reservoir Production Forecast

Analytical models, in particular Decline Curve Analysis (DCA) are widely used in the oil and gas industry. However, they are often solely based on production data from the declining wells and do not leverage the other data available in the field e.g. petrophysics at well, completion length, distance to contacts... This paper describes a workflow to quickly build hybrid models for reservoir production forecast based on a mix of classic reservoir methods and machine learning algorithms. This workflow is composed of three main steps applied on a well by well basis. First, we build an object called forecaster which contains the subject matter knowledge. This forecaster can represent parametric functions trained on the well itself or more complex models that learn from a larger data set (production and petrophysics data, synthesis properties). Secondly this forecaster is tested on a subset of production history to qualify it. Finally, the full data set is used to forecast the production profile. It has been applied to all fluids (oil, water, gas, liquid) and revealed particularly useful for fields with large number of wells and long history, as an alternative to classical simulations when grid models are too complex or difficult to history match. Two use cases from conventional and unconventional fields will be presented in which this workflow helped quickly generate robust forecast for existing wells (declining or non-declining) and new wells. This workflow brings the technology, structure and measurability of Data Science to Reservoir Engineering. It enables the application of the state of the art data science methods to solve concrete reservoir engineering problems. In addition, forecast results can be confronted to historical data using what we call "Blind Testing" which allows a quantification of the forecast uncertainty and avoid biases. Finally, the automated workflow has been used to generate a range of possible realizations and allows the quantification the uncertainty associated with the models.

Aspects of Multiscale Flow Simulation with Potential to Enhance Reservoir Engineering Practice

Summary A multiscale sequential fully implicit (MS SFI) reservoir simulation method implemented in a commercial simulator is applied to a set of reservoir engineering problems to understand its potential. Our assessment highlights workflows where the approach brings substantial performance advantages and insight generation. The understanding gained during commercialization on approximately 40 real-world models is illustrated through simpler but representative data sets, available in the public domain. The main characteristics of the method and key features of the implementation are briefly discussed. The robust fully implicit (FI) simulation method is used as a benchmark. The implementation of the MS SFI method is found to faithfully reproduce FI results for black-oil problems. We provide evidence and analysis of why the MS SFI approach can achieve high levels of performance and fidelity. The method supports the solution of unique problems that would benefit from incorporating multiscale geology and multiscale flow physics. The MS SFI implementation was used to successfully simulate a typical sector model used for field pilots at extremely high “whole core” scale resolution within a practical time frame leveraging high-performance computing (HPC). This could not be achieved with the FI approach. A combination of MS SFI and HPC offers immense potential to simulate geological models using grids to capture mesoscopic or laminar scale geology. The method, by design, demands fewer computing resources than FI, making it far more cost-effective to use for such high-resolution models. We conclude that the MS SFI method has a distinct capability to enhance reservoir engineering practice in the areas of high-resolutionsimulation-driven workflows in context of subsurface uncertainty quantification, field development planning, and reservoir performance optimization. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Aspects of Multiscale Flow Simulation with Potential to Enhance Reservoir Engineering Practice

A new implementation of a multiscale sequential fully implicit (MS SFI) reservoir simulation method is applied to a set of reservoir engineering problems to understand its utility. An assessment is made to highlight areas where the approach brings substantial advantage in performance as well as address problems not successfully resolved by existing methods. This work makes use of the first ever implementation of the multiscale sequential fully implicit method in a commercial reservoir simulator. The key features of the method and implementation are briefly discussed. The learnings gained during field testing and commercialization on about forty real world models is illustrated through simpler, but representative data sets, available in the public domain. The workhorse robust fully implicit (FI) method is used as a reference for benchmarking. The MS SFI method can faithfully reproduce FI results for black oil problems. We conclude that the MS SFI method has the capability to support reservoir engineering decision making especially in the areas of subsurface uncertainty quantification, representative model selection, model calibration and optimization. The MS SFI method shows immense potential for handling prominent levels of reservoir heterogeneity. The challenge of including fine-scale heterogeneity, which is often overlooked, when scaling up EOR processes from laboratory to field, appears to have found a practical solution with a combination of MS SFI and high-performance computing (HPC).

Physics Inspired Machine Learning for Solving Fluid Flow in Porous Media: A Novel Computational Algorithm for Reservoir Simulation

The Physics Inspired Machine Learning (PIML) is emerging as a viable numerical method to solve partial differential equations (PDEs). Recently, the method has been successfully tested and validated to find solutions to both linear and non-linear PDEs. To our knowledge, no prior studies have examined the PIML method in terms of their reliability and capability to handle reservoir engineering boundary conditions, fractures, source and sink terms. Here we explored the potential of PIML for modelling 2D single phase, incompressible, and steady state fluid flow in porous media. The main idea of PIML approaches is to encode the underlying physical law (governing equations, boundary, source and sink constraints) into the deep neural network as prior information. The capability of the PIML method in handling reservoir engineering boundary including no-flow, constant pressure, and mixed reservoir boundary conditions is investigated. The results show that the PIML performs well, giving good results comparable to analytical solution. Further, we examined the potential of PIML approach in handling fluxes (sink and source terms). Our results demonstrate that the PIML fail to provide acceptable prediction for no-flow boundary conditions. However, it provides acceptable predictions for constant pressure boundary conditions. We also assessed the capability of the PIML method in handling fractures. The results indicate that the PIML can provide accurate predictions for parallel fractures subjected to no-flow boundary. However, in complex fractures scenario its accuracy is limited to constant pressure boundary conditions. We also found that mixed and adaptive activation functions improve the performance of PIML for modeling complex fractures and fluxes.

A Thorough Review of Machine Learning Applications in Oil and Gas Industry

Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.

Applications of Honey Bee Optimization in Reservoir Engineering Assisted History Matching

Bee colony optimization technique is a stochastic population-based optimization algorithm inspired by the natural optimization behavior shown by honey bees during searching for food. Bee colony optimization algorithm has been successfully applied to various real-world optimization problems mostly in routing, transportation, and scheduling fields. This paper introduces the bee colony optimization method as the optimization technique in reservoir engineering assisted history matching procedure. The superiority of the proposed optimization algorithm is validated by comparing its performance with two other advanced nature-inspired optimization techniques (genetic and particle swarm optimization algorithms) in three synthetic assisted history matching problems. In addition, this paper presents the application of the bee colony optimization technique in assisting the history match of a full field reservoir simulation model of a mature gas-cap reservoir with 28 years of history. The resultant history matched model is compared with those obtained using a manual history matching procedure and using the most widely applied optimization algorithm used in assisted history matching commercial software tools. The results of this work indicate that employing the bee colony algorithm as the optimization technique in the assisted history matching workflow yields noticeable enhancement in terms of match quality and time required to achieve a reasonable match.