{"id":1900,"date":"2026-05-28T14:40:22","date_gmt":"2026-05-28T18:40:22","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/?p=1900"},"modified":"2026-06-23T05:10:43","modified_gmt":"2026-06-23T09:10:43","slug":"digital-crude-oil-models-design-exploration-at-scale-for-oil-and-gas-innovation","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/2026\/05\/28\/digital-crude-oil-models-design-exploration-at-scale-for-oil-and-gas-innovation\/","title":{"rendered":"Digital crude oil models: Design exploration at scale for oil and gas innovation"},"content":{"rendered":"\n<p>In oil and gas, the challenge is no longer only to understand crude oil: it is to <strong>explore, compare, and redesign chemical systems faster than experimentation allows<\/strong>. Whether the goal is flow assurance, formulation screening, chemical production or materials optimization, innovation today depends on how efficiently complex crude systems can be represented, modified and explored in silico.<\/p>\n\n\n\n<p>This is where <strong>digital crude oil models<\/strong> come into play. Not as static representations, but as <strong>living, design-ready models<\/strong> that engineers can manipulate, adapt and connect to automated exploration workflows. With <a href=\"https:\/\/www.siemens.com\/en-us\/products\/simcenter\/fluids-thermal-simulation\/culgi\/\" target=\"_blank\" rel=\"noopener\"><strong>Simcenter Culgi<\/strong><\/a>, digital crude oil modeling becomes a practical, scalable foundation for chemical design exploration across the oil and gas value chain.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">From crude oil complexity to actionable digital models<\/h2>\n\n\n\n<p>Crude oil is one of the most complex industrial fluids: thousands of components, broad distributions of molecular sizes and functionalities, and strong sensitivity to temperature, pressure and composition. Modeling such systems at full atomistic resolution is neither necessary nor efficient for most engineering questions.<\/p>\n\n\n\n<p>Modern digital crude oil models take a <strong>fit-for-purpose approach<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Preserve the <strong>chemical diversity that drives macroscopic behavior<\/strong><\/li>\n\n\n\n<li>Reduce unnecessary molecular detail<\/li>\n\n\n\n<li>Focus on properties that matter for engineering decisions<\/li>\n<\/ul>\n\n\n\n<p>In Simcenter Culgi, crude oils are represented using <strong>experimentally grounded digital models<\/strong>, typically built from:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>SARA fractions (saturates, aromatics, resins, asphaltenes)<\/li>\n\n\n\n<li>Density and compositional ranges<\/li>\n\n\n\n<li>Acid number (TAN) and polar content<\/li>\n\n\n\n<li>Additional field\u2011relevant descriptors when needed<\/li>\n<\/ul>\n\n\n\n<p>The result is <strong>a digital crude oil that behaves like the real one<\/strong>, but is computationally tractable and, crucially, <strong>easy to modify, recombine and explore <\/strong>with our Mixture editor capability:<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video autoplay muted src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/35\/2026\/05\/MolecularEditor.mp4\"><\/video><\/figure>\n\n\n\n<p>This is not about recreating every molecule. It is about enabling <strong>design exploration on systems that remain chemically relevant<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Full crude or targeted chemistry: One framework, multiple scales<\/h2>\n\n\n\n<p>A key strength of this approach is flexibility. The same digital chemistry framework supports:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Full crude oil systems<\/strong>, when global behavior matters<br>(phase stability, aggregation, interfacial properties, flow behavior)<\/li>\n\n\n\n<li><strong>Targeted chemical subsystems<\/strong>, when the focus shifts to:\n<ul class=\"wp-block-list\">\n<li>Asphaltenes and resins<\/li>\n\n\n\n<li>Surfactants and additives<\/li>\n\n\n\n<li>Solvents and demulsifiers<\/li>\n\n\n\n<li>Lubricants and specialty chemicals derived from crude fractions<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>This is particularly important for organizations that operate <strong>beyond extraction<\/strong>, where crude oil is also a <strong>feedstock for chemical production<\/strong>. Digital models can be refined to focus on the molecular families that matter for performance, stability, or manufacturability \u2013 without rebuilding the system from scratch.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Asphaltenes: From problem management to design variable<\/h2>\n\n\n\n<p>Asphaltenes remain one of the most expensive challenges in oil and gas operations. Their aggregation behavior impacts flow assurance, processing, and long\u2011term operability, that said, they are notoriously difficult to characterize experimentally.<\/p>\n\n\n\n<p>With multiscale simulation in Simcenter Culgi, asphaltenes are no longer treated as a dark box. Digital crude oil models enable engineers to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Compare aggregation tendencies across crude compositions<\/li>\n\n\n\n<li>Study the impact of processing history and solvent environment<\/li>\n\n\n\n<li>Analyze interactions with additives at a molecular level<\/li>\n<\/ul>\n\n\n\n<p>More importantly, <a href=\"https:\/\/blogs.sw.siemens.com\/energy-utilities\/2026\/02\/06\/advancing-molecular-level-engineering-chevrons-use-of-simcenter-culgi-for-enhanced-asphaltene-understanding\/\"><strong>asphaltenes become a design variable<\/strong><\/a>, not just a problem to mitigate. Digital chemistry opens the door to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Rational screening of inhibitors and dispersants<\/li>\n\n\n\n<li>Evaluation of processing strategies before deployment<\/li>\n\n\n\n<li>Exploration of alternative valorization pathways (e.g., materials, additives)<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/35\/2026\/05\/VirginAsphaltene-ezgif.com-resize-video.mp4\"><\/video><figcaption class=\"wp-element-caption\"><em>Digital crude oil models enable engineers to analyze virgin asphaltenes as shown on the left to processed asphaltenes as shown on the right. <\/em><\/figcaption><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/35\/2026\/05\/ProcessedAsphaltene.mp4\"><\/video><\/figure>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Design exploration first: Screening beats trial and error<\/h2>\n\n\n\n<p>Historically, many digital chemistry workflows in oil and gas were designed as <strong>one\u2011off studies<\/strong>, often based on methods and assumptions developed several years ago. That legacy approach tends to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Focus on single formulations<\/li>\n\n\n\n<li>Emphasize validation over exploration<\/li>\n\n\n\n<li>Underuse automation and design space exploration<\/li>\n<\/ul>\n\n\n\n<p>Modern workflows shift the paradigm: <strong>screen first, refine later<\/strong>.<\/p>\n\n\n\n<p>With Simcenter Culgi, formulation design becomes a <strong>screening problem<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Hundreds of compositions explored virtually<\/li>\n\n\n\n<li>Key performance indicators computed consistently<\/li>\n\n\n\n<li>Poor candidates eliminated early, before synthesis or lab testing<\/li>\n<\/ul>\n\n\n\n<p>Petronas and other operators have demonstrated how <strong>digital crude oil models can be used to screen formulation families<\/strong>, rather than optimize a single recipe. <a href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/oil-and-gas-computational-chemistry-methods\/\">See how Petronas decreased formulation development by 85% using Simcenter Culgi<\/a>. This approach dramatically reduces experimental load while increasing confidence in the final candidates. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>From digital chemistry to automated design with HEEDS<\/strong><\/h2>\n\n\n\n<p>The real acceleration happens when digital crude oil models are connected to <strong>automated design exploration<\/strong>.<\/p>\n\n\n\n<p>By coupling Simcenter Culgi with <strong>HEEDS<\/strong>, organizations can:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Explore large formulation design spaces automatically<\/li>\n\n\n\n<li>Optimize multiple objectives simultaneously (performance, robustness, cost)<\/li>\n\n\n\n<li>Identify non\u2011intuitive solutions that manual tuning would miss<\/li>\n<\/ul>\n\n\n\n<p>This combination transforms digital chemistry from a simulation tool into a <strong>decision engine<\/strong>, capable of guiding R&amp;D and operational choices at scale.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why does digital chemistry matter now?<\/h2>\n\n\n\n<p>Some earlier digital chemistry narratives in oil and gas were built on workflows developed 4\u20135 years ago, when automation was limited and models were often static. Today, the landscape has changed:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Coarse\u2011grained methods are more mature<\/li>\n\n\n\n<li>Automation and AI\u2011assisted exploration are operational<\/li>\n\n\n\n<li>Integration across simulation, optimization and data workflows is practical<\/li>\n<\/ul>\n\n\n\n<p>The focus is no longer on proving that digital chemistry works. It is about <strong>using it systematically to explore, screen, and design better systems faster<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Digital crude oil as a strategic asset<\/h2>\n\n\n\n<p>Digital crude oil models are no longer academic constructs or isolated studies. When implemented in Simcenter Culgi, they become:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Reusable<\/li>\n\n\n\n<li>Adaptable<\/li>\n\n\n\n<li>Scalable across applications<\/li>\n\n\n\n<li>Directly connected to design exploration workflows<\/li>\n<\/ul>\n\n\n\n<p>Whether the goal is optimizing production, mitigating risks, or designing new chemical products derived from crude oil, <strong>digital chemistry provides a competitive advantage<\/strong>, &nbsp;not by replacing experiments, but by ensuring that only the right experiments are performed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Ready to learn more about digital chemistry? <\/h2>\n\n\n\n<p>Simcenter Culgi offers a versatile, dynamic platform to simulate the most complex systems, faster. Explore this topic further on the <a href=\"https:\/\/www.plm.automation.siemens.com\/global\/en\/products\/simcenter\/culgi.html\" target=\"_blank\" rel=\"noopener\">Simcenter Culgi website<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><a id=\"_msocom_1\"><\/a>                                                                                                                         <\/p>\n","protected":false},"excerpt":{"rendered":"<p>In oil and gas, the challenge is no longer only to understand crude oil: it is to explore, compare, and&#8230;<\/p>\n","protected":false},"author":81923,"featured_media":1902,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"true","german_translation":"true","italian_translation":"true","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[67,326],"industry":[39],"product":[],"coauthors":[562],"class_list":["post-1900","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-digitalization","tag-energy-utilities","industry-energy-utilities"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/35\/2026\/05\/AES-3-scaled.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/posts\/1900","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/users\/81923"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/comments?post=1900"}],"version-history":[{"count":5,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/posts\/1900\/revisions"}],"predecessor-version":[{"id":1942,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/posts\/1900\/revisions\/1942"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/media\/1902"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/media?parent=1900"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/categories?post=1900"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/tags?post=1900"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/industry?post=1900"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/product?post=1900"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/energy-utilities\/wp-json\/wp\/v2\/coauthors?post=1900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}