An advanced workflow to compress the uncertainties of stochastic distribution of Bahariya reservoir properties using 3D static modeling:An example from Heba Oil Fields,Western Desert,Egypt

The main objective of this paper is to construct a static model that compress the uncertainties of the stochastic distribution of the reservoir properties of the Bahariya Formation in Heba field,at the northeastern portion of the Western Desert.This model has been constructed through the integration of the interpretations of the eighteen 2D seismic sections and the analysis of well logs data for four wells(HEBA 300X,E.BAH-E-1X,E.BAH-D-1X,and HEBA 10X)drilled in the study area.This set of data was implemented in a harmonic workflow.Structural framework was the first step created on the basis of the seismic and well log interpretations.Model zonation was mainly managed by the marine flooding events took place during the Cenomanian period.The trapping faults position uncertainty has been compressed through the tying of the seismic profiles with the identified fault cuts in the well data.Effective porosity spectrum was broke up into three reservoir qualities.The results showed heterogeneous facies qualities for oil production in specific five zones in the topmost part of the Bahariya Formation.The effective porosity model was generated stochastically considering the normal distribution for each reservoir quality.Water saturation was distributed by two methods;1)Sequential Gaussian Simulation that was co-simulated by porosity model.2)Log-based saturation height function for each reservoir quality.This methodology provided as accurate as possible estimates for the volume calculation by quantifying the sensitivity of the important parameters such as oil contact.Additionally,the model was prepared to be used as a front end for dynamic simulation.

Distributed Stochastic Optimization with Compression for Non-Strongly Convex Objectives

We are investigating the distributed optimization problem,where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions.Since nodes exchange optimization parameters through the wireless network,large-scale training models can create communication bottlenecks,resulting in slower training times.To address this issue,CHOCO-SGD was proposed,which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective functions.Nevertheless,most convex functions are not strongly convex(such as logistic regression or Lasso),which raises the question of whether this algorithm can be applied to non-strongly convex functions.In this paper,we provide the first theoretical analysis of the convergence rate of CHOCO-SGD on non-strongly convex objectives.We derive a sufficient condition,which limits the fidelity of compression,to guarantee convergence.Moreover,our analysis demonstrates that within the fidelity threshold,this algorithm can significantly reduce transmission burden while maintaining the same convergence rate order as its no-compression equivalent.Numerical experiments further validate the theoretical findings by demonstrating that CHOCO-SGD improves communication efficiency and keeps the same convergence rate order simultaneously.And experiments also show that the algorithm fails to converge with low compression fidelity and in time-varying topologies.Overall,our study offers valuable insights into the potential applicability of CHOCO-SGD for non-strongly convex objectives.Additionally,we provide practical guidelines for researchers seeking to utilize this algorithm in real-world scenarios.

RB-DEM Modeling and Simulation of Non-Persisting Rough Open Joints Based on the IFS-Enhanced Method

When the geological environment of rock masses is disturbed,numerous non-persisting open joints can appear within it.It is crucial to investigate the effect of open joints on the mechanical properties of rock mass.However,it has been challenging to generate realistic open joints in traditional experimental tests and numerical simulations.This paper presents a novel solution to solve the problem.By utilizing the stochastic distribution of joints and an enhanced-fractal interpolation system(IFS)method,rough curves with any orientation can be generated.The Douglas-Peucker algorithm is then applied to simplify these curves by removing unnecessary points while preserving their fundamental shape.Subsequently,open joints are created by connecting points that move to both sides of rough curves based on the aperture distribution.Mesh modeling is performed to construct the final mesh model.Finally,the RB-DEM method is applied to transform the mesh model into a discrete element model containing geometric information about these open joints.Furthermore,this study explores the impacts of rough open joint orientation,aperture,and number on rock fracture mechanics.This method provides a realistic and effective approach for modeling and simulating these non-persisting open joints.

Stochastic and Distributed Optimal Energy Management of Active Distribution Networks Within Integrated Office Buildings

This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning(HVAC)systems,and electric vehicle(EV)charging piles,to participate in the ADN optimization.First,an energy management approach for the ADN with aggregated office buildings is proposed.And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers.Then,to consider un-certainties of photovoltaic(PV)power,scenario-based stochastic programming is integrated into the ADN optimization model.To further realize the stochastic energy management of the ADN within office buildings in a distributed manner,the alternating direction method of multipliers(ADMM)is used to solve the ADN optimization model.The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings.Finally,the ADN optimization model incorporating office buildings is validated in the winter by numerical studies.In addition,the impacts of comfort temperature range and expected state of charge(SOC)at departure are analyzed.Index Terms—ADN,EV,HVAC system,Office building,Stochastic and distributed energy management.

Distribution dependent reflecting stochastic differential equations

To characterize the Neumann problem for nonlinear Fokker-Planck equations,we investigate distribution dependent reflecting stochastic differential equations(DDRSDEs)in a domain.We first prove the well-posedness and establish functional inequalities for reflecting stochastic differential equations with singular drifts,and then extend these results to DDRSDEs with singular or monotone coefficients,for which a general criterion deducing the well-posedness of DDRSDEs from that of reflecting stochastic differential equations is established.

Distributed Heterogeneous Multi-Agent Optimization with Stochastic Sub-Gradient

This paper studies the optimization problem of heterogeneous networks under a timevarying topology.Each agent only accesses to one local objective function,which is nonsmooth.An improved algorithm with noisy measurement of local objective functions' sub-gradients and additive noises among information exchanging between each pair of agents is designed to minimize the sum of objective functions of all agents.To weaken the effect of these noises,two step sizes are introduced in the control protocol.By graph theory,stochastic analysis and martingale convergence theory,it is proved that if the sub-gradients are uniformly bounded,the sequence of digraphs is balanced and the union graph of all digraphs is joint strongly connected,then the designed control protocol can force all agents to find the global optimal point almost surely.At last,the authors give some numerical examples to verify the effectiveness of the stochastic sub-gradient algorithms.

Controlling the probability density of quantum systems

A new method to design a quantum controller which directly controls the probability density function（PDF） of quantum systems is proposed.Based on the quantum model from the PDF perspective,two specific control algorithms are proposed with uniform and non-uniform fields,respectively.Then a detailed control algorithm with convergence analysis is given for the small error case.By appropriately estimating the selected Lyapunov function,more accurate control effect is achieved.The proposed scheme provides a constructive method to find appropriate parameters for controller design.

Distribution dependent stochastic differential equations

Due to their intrinsic link with nonlinear Fokker-Planck equations and many other applications,distribution dependent stochastic differential equations(DDSDEs)have been intensively investigated.In this paper,we summarize some recent progresses in the study of DDSDEs,which include the correspondence of weak solutions and nonlinear Fokker-Planck equations,the well-posedness,regularity estimates,exponential ergodicity,long time large deviations,and comparison theorems.

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors＇ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.