Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Fast mass transport of oil and supercritical carbon dioxide through organic nanopores in shale

Current understanding of shale wettability: A review on contact angle measurements

Occurrence space and state of shale oil: A review

Movable oil content evaluation of lacustrine organic-rich shales: Methods and a novel quantitative evaluation model

Characterization of Microscopic Pore Structures in Shale Gas Reservoirs of the Southeast Chongqing

Characterization on Micro-pore Structure of Tight Reservoir in the 4th Member of the Shahejie Formation in the Damintun Sag by Laser Confocal Scanning Microscope

mud shale and oil volume is mainly influenced by geochemical characteristics of source rock (the abundance, type and maturity), workability mainly influenced by reconstructive fracturing of rock. Now the ministry of land and resources strategic research center and the main oil and gas fields are carrying out the resources evaluation in the country and total acreage of shale oil and gas evaluation, preliminary results revealed that Chinese continental shale contains huge amounts of shale oil resources, such as Qing 1 segment in the south of Songliao basin up to 8 billion tons, the bottom of E2S3 segment of south bohai depression in bohai bay basin up to 4 billion tons. But the shale has tight, low porosity, especially low permeability, and the density and viscosity of oil is larger than gas, so oil flows more difficultly than gas in the underground. The key to restrict the effective of shale oil exploration and development is not the oil volume of the shale , but how much mobile (recoverable) ability in it, therefore, shale oil mobility evaluation is critical. In theory, classification of shale oil and gas resources should be based on the enrichment degree and the workability of oil and gas, but as a result of oil and gas (economic) workability is related to technology, the price of oil, and so on. The degree of enrichment is the basis and premise of (economic) workability, therefore, regard the enrichment of oil and gas as the first element of grading resource evaluation. The division of enrichment period is according to the relationship of geochemistry parameters in abundance of organic matter, the division standard is as follows: Based on TOC — chloroform bitumen \"A\" and the TOC — pyrolysis S1 scatterplot (Fig. 1), chloroform bitumen \"A\" and pyrolysis S1 increases along with the TOC, while the process of increasing has three stages, divided into stable low section, rising section and stability section of high value. On this basis, I draw the envelope curve of the TOC — chloroform bitumen \"A\" and the TOC — pyrolysis S1 scatterplot and determine the TOC boundaries between the three sections, as the TOC classification standard of resource grading evaluation of shale oil, stable low segment is dispersion (invalid) resources (level III), the rising of inefficient resource (level II), stability of high value is the enrichment resources (level I). The turning point between the stabilize low and rise sections of the envelope curve and the midpoint of rising section correspond to the boundaries of the chloroform bitumen \"A\" and the pyrolysis S1, and draw the boundaries as the chloroform bitumen \"A\" and pyrolysis S1 classification standard, if the application in the process of TOC, chloroform bitumen \"A\" and pyrolysis S1 classification standard appeared antinomy, the TOC classification standard shall prevail. Due to the particularity of the object and the lack of experimental instrument which is specific to the object, the related research lags behind. And the theory, method and experiment are lagging behind the exploration and practice. It is generally acknowledged that the higher is the abundance of organic matter in mud shale, the greater is the amount of shale oil, but it should not be ignored that the more is organic adsorption of oil. Whether adsorption oil can be desorbed and the amount of the movable oil linear increases with the organic matter abundance or not, how to evaluate them in the experiment or in the method, there is no related reports. Foreign scholars, according to the exploration and practice of shale oil wells, put forward that if 100 x S1 / TOC of mud shale section is greater than 100, this part is the good shale oil output interval. Mud shale entering the hydrocarbon expulsion threshold means the raw hydrocarbons have made the shale reach WANG Xin, LU Shuangfang, CHEN Guohui and LI Jinbu, 2015. Resources Potential Evaluation of Movable Shale Oil in the E2S4 Enrichment Segment of Damintun. Acta Geologica Sinica (English Edition), 89(supp.): 324-325.

Resources Potential Evaluation of Movable Shale Oil in the E2S4 Enrichment Segment of Damintun

been very important in the unconventional oil and gas exploration. In the different basin , the accurate description of the shales organic carbon content in the vertical and horizontal distribution characteristics,having a very important influence on the evaluation of the hydrocarbon source rock, the calculation of the hydrocarbon generation and expulsion efficiency, even the prediction of hydrocarbon distribution and so on. At present, organic heterogeneity evaluation methods the domestic and international is as follow: the logR

Study of Organic Heterogeneity Evaluation Methods

organic carbon(TOC)content in mud shale:geochemical analysis and ΔlgR model. Geochemical analysis can obtain accurate TOC content, but it has the problem of higher cost and less samples. The ΔlgR model can obtain TOC content variable values in well profile conveniently and quickly, but it can’t predict TOC content in mud shale in cross-hole area. Both the methods can’t meet the needs of resources evaluation of shale oil in the back ground of strong organic heterogeneous in mud shale. We propose a new method for predicting TOC content based on the improved ΔlgR model and geostatistical inversion. The method combines good lateral resolution of seismic data and high vertical resolution of logging data, and can predict the spatial distribution of TOC in mud shale accurately. It has great practical significance for the exploration of shale oil. The new method requires to obtain TOC variable values in well profile at first. In the traditional ΔlgR model, acoustic travel time and resistivity curves are overlapped together artificially with a fix overlapping coefficient, then defined the baseline value and background content of TOC to calculate ΔlgR and TOC content. The traditional ΔlgR model has several deficiencies on applicability, objectivity, convenience from the aspects of baseline value, background content of TOC and overlapping coefficient. Therefore, we improve the ΔlgR model based on the theory of the traditional ΔlgR model. The improved ΔlgR model can select overlapping coefficient automatically, obtain TOC content accurately and rapidly by computer in absence of mature degree parameters, and manual definition of TOC background value. Haas (1994)proposed geostatistical inversion firstly, and Dubrule(1998)and Rothman(1998)further developed this theory. Its fundamental process is: starting from well point, the inversion is faithful to logging data at well point and conforms to seismic data in cross-hole area. Geostatistical inversion is generally used to predict the spatial distribution of sandstone and obtains good application effects. Using the geostatistical inversion to calculate TOC content requires to analysis the relaxationship between p-wave impedance and TOC content in mud shale. Since organic-rich mud shale poses to less density, higher acoustic interval travel time and lower velocity, obviously, p-wave impedance decreases with the increasing of TOC content in mud shale(Fig.1).

Prediction of Total Organic Carbon Content Based on Geostatistical Inversion: A Case of Qingshankou Formation in Wangfu Depression, Songliao Basin

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Shuangfang Lu

Author Tools

Chat about Author