{"id":56708,"date":"2022-10-24T06:00:09","date_gmt":"2022-10-23T22:00:09","guid":{"rendered":"http:\/\/learncmg.cn\/?p=56708"},"modified":"2022-10-24T06:00:09","modified_gmt":"2022-10-23T22:00:09","slug":"184%ef%bc%9a%e4%bb%8e%e5%9c%b0%e9%9c%87%e5%88%b0%e6%95%b0%e5%80%bc%e6%a8%a1%e6%8b%9f%ef%bc%9a%e7%be%8e%e5%9b%bd%e4%bf%84%e5%85%8b%e6%8b%89%e8%8d%b7%e9%a9%ac%e5%b7%9e%e4%bc%8d%e5%be%b7%e7%a6%8f","status":"publish","type":"post","link":"http:\/\/learncmg.cn\/?p=56708","title":{"rendered":"184. \u4ece\u5730\u9707\u5230\u6570\u503c\u6a21\u62df\uff1a\u7f8e\u56fd\u4fc4\u514b\u62c9\u8377\u9a6c\u5dde\u4f0d\u5fb7\u798f\u5fb7\u9875\u5ca9\u6848\u4f8b\u7814\u7a76"},"content":{"rendered":"

Seismic to Simulation: Woodford Shale Case Study in Oklahoma, USA<\/a><\/h3>\n

\u6458\u8981<\/p>\n

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Our results show, the integration from seismic to production scale provides excellent control on the reservoir characterization and bolsters confidence on the static model. Out of all clustering techniques, SOM works best in our case and is consistent with the regional geology. More number of samples in case of seismic provides better horizontal control on geomechanical properties and hence modeling of the SRV and ultimately predicting production behavior.
\nAbstract
\nThe objective of our study is to use data from every scale for reservoir characterization and eventually reservoir simulation. The current area of study is Woodford Shale in Oklahoma. Due to enormous pay zone and thus oil and gas reserves, Woodford Shale is the center of attraction for major oil operators in Oklahoma currently.
\nIn this study we have used well logs to obtain high vertical resolution petrophysical and geomechanical properties such as: Porosity, total organic carbon (TOC), Young\u2019s Modulus, Shear Modulus, Biot\u2019s coefficient, Poisson\u2019s ratio, stresses etc. We have used seismic inversion derived impedance volumes to populate these properties in the model area to obtain better stratigraphic control on these properties. To obtain the type curves (TC\u2019s), we have used unsupervised machine learning methodologies such as Self Organizing maps (SOM) and generative topographic mapping (GTM) to cluster the reservoir properties together.
\nFrom these clusters, we have identified four type curve (TC) areas. To demonstrate the physical significance of these four type curve areas, we have used a fully compositional, fully coupled geomechanical Equation of State (EOS) simulator to simulate the Stimulated Rock Volume (SRV) as a function of stress and eventually production.
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\nReservoir Simulation We have selected four type curve areas for simulation in our study based on the seismic and well logs derived clusters. We use a 5,000 Ft. lateral length of the wells in our area to perform our simulation based on most accepted industry standards in vicinity areas. To fully capture the fluid effects, we use a fully compositional rich condensate representative PVT in the area.
\nWe use the same fluid in all the areas as the primary aim of this study is to identify the type curves based on reservoir properties that can be known ahead of the drilling and not the fluid properties which can vary and alter the Recovery Factors (RF\u2019s). For geomechanical coupling, we use a fully coupled Barton-Bandis model (Bandis et al., 1983) in a dual porosity setup. Figure 13 shows the conceptual implementation of this model in which the permeability of the natural factures is coupled in the model as the minimum effective stress. As the injection is carried out in a typical hydraulic fracturing scenario, the minimum effective stress decreases, eventually increasing the permeability of the natural fractures.
\n\"\u56fe\u7247\u5305\u542b
\n<\/p>\n","protected":false},"excerpt":{"rendered":"

Seismic to Simulation: Woodford Shale Case Study iContinue Reading<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[159],"tags":[55],"_links":{"self":[{"href":"http:\/\/learncmg.cn\/index.php?rest_route=\/wp\/v2\/posts\/56708"}],"collection":[{"href":"http:\/\/learncmg.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/learncmg.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/learncmg.cn\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/learncmg.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=56708"}],"version-history":[{"count":0,"href":"http:\/\/learncmg.cn\/index.php?rest_route=\/wp\/v2\/posts\/56708\/revisions"}],"wp:attachment":[{"href":"http:\/\/learncmg.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=56708"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/learncmg.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=56708"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/learncmg.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=56708"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}