{"id":45784,"date":"2021-09-20T10:23:03","date_gmt":"2021-09-20T17:23:03","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45784"},"modified":"2025-03-28T12:50:55","modified_gmt":"2025-03-28T19:50:55","slug":"paice-builder-use-case","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/paice-builder-use-case","title":{"rendered":"PAICe Builder Use Case"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141\">\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-parallax .av-parallax-inner{\nbackground-repeat:no-repeat;\nbackground-image:url(\/wp-content\/uploads\/2025\/03\/resources-banner-1500x333.jpg);\nbackground-position:50% 50%;\nbackground-attachment:scroll;\n}\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-section-color-overlay{\nopacity:0.3;\nbackground-color:#000000;\n}\n<\/style>\n<div id='av_section_1'  class='avia-section av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 main_color avia-section-default avia-no-border-styling  avia-builder-el-0  el_before_av_one_full  avia-builder-el-first  av-parallax-section avia-bg-style-parallax av-section-color-overlay-active av-minimum-height av-minimum-height-25 av-height-25  container_wrap fullsize'  data-section-bg-repeat='no-repeat' data-av_minimum_height_pc='25' data-av_min_height_opt='25'><div class='av-parallax' data-avia-parallax-ratio='0.3' ><div class='av-parallax-inner main_color avia-full-stretch'><\/div><\/div><div class=\"av-section-color-overlay-wrap\"><div class=\"av-section-color-overlay\"><\/div><div class='container av-section-cont-open' ><main  role=\"main\" itemprop=\"mainContentOfPage\"  class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-45784'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ig608p-4f8d1c0453663a84f522f5ebe321d648\">\n.flex_column.av-ig608p-4f8d1c0453663a84f522f5ebe321d648{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ig608p-4f8d1c0453663a84f522f5ebe321d648 av_two_third  avia-builder-el-1  el_before_av_one_third  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a\">\n#top .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a{\npadding-bottom:10px;\ncolor:#ffffff;\nfont-size:40px;\n}\nbody .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:40px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .special-heading-inner-border{\nborder-color:#ffffff;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:25px;\n}\n\n@media only screen and (min-width: 768px) and (max-width: 989px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:50px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:25px;\n}\n}\n\n@media only screen and (min-width: 480px) and (max-width: 767px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:47px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:20px;\n}\n}\n\n@media only screen and (max-width: 479px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:33px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:15px;\n}\n}\n<\/style>\n<div  class='av-special-heading av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a av-special-heading-h1 custom-color-heading blockquote modern-quote  avia-builder-el-2  avia-builder-el-no-sibling  av-inherit-size av-linked-heading'><h1 class='av-special-heading-tag '  itemprop=\"headline\"  >PHYSICS, MACHINES AND DATA<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2pyrzd-6af470dabd56f3b979785905ac98eceb\">\n.flex_column.av-2pyrzd-6af470dabd56f3b979785905ac98eceb{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-2pyrzd-6af470dabd56f3b979785905ac98eceb av_one_third  avia-builder-el-3  el_after_av_two_third  avia-builder-el-last  flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a\">\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a av-styling- avia-align-center  avia-builder-el-4  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45060 avia-img-lazy-loading-not-45060 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png\" alt='Tignis, A Cohu Analytics Solution logo in white' title='Tignis-Logo-cohu-tagline-all white-transparent'  height=\"89\" width=\"300\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png 300w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1030x305.png 1030w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-768x228.png 768w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1536x455.png 1536w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-2048x607.png 2048w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1500x444.png 1500w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-705x209.png 705w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/div><\/div><\/div><\/div>\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div><\/div><div id='after_section_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-45784'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-2y29t5-8eac002ebd25dfff14a3d2eed1a49041 av_one_full  avia-builder-el-5  el_after_av_section  el_before_av_post_metadata  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266\">\n#top .av_textblock_section.av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 .avia_textblock{\nfont-size:40px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"text-align: center;\"><strong>PAICe Builder Use Case: Predictive rod pump analytics improve efficiency, oil production, and revenue<\/strong><\/p>\n<\/div><\/section><\/div><div  class='av-post-metadata-container av-av_post_metadata-f2d93833baf55c47e6bd6c86e40f308e av-metadata-container-align-center  avia-builder-el-7  el_after_av_one_full  el_before_av_hr  av-metadata-container-1'><div class='av-post-metadata-container-inner'><span class=\"av-post-metadata-content av-post-metadata-meta-content\"><span class=\"av-post-metadata-content av-post-metadata-published\"><span class=\"av-post-metadata-published-date\" >20. September 2021<\/span><\/span><span class=\"av-post-metadata-content av-post-metadata-separator\">|<\/span><span class=\"av-post-metadata-content av-post-metadata-category\"><span class=\"av-metadata-before av-metadata-before-categories\">in<\/span><span class=\"av-post-metadata-category-link\" ><a href=\"https:\/\/www.cohu.com\/category\/thought-leadership\/\" >Thought Leadership<\/a><\/span><\/span><\/span><\/div><\/div><\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t23l8k-62a6eba0a8ac6fc71ae62cc5e5d4d021\">\n#top .hr.hr-invisible.av-m8t23l8k-62a6eba0a8ac6fc71ae62cc5e5d4d021{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8t23l8k-62a6eba0a8ac6fc71ae62cc5e5d4d021 hr-invisible  avia-builder-el-8  el_after_av_post_metadata  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-1ltc3hq-d678e11ab7342dc0fff705952edff81f av_one_full  avia-builder-el-9  el_after_av_hr  el_before_av_hr  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-14x5t1q-e31ae190fad06918677f3dc6f7e80321\">\n.avia-image-container.av-14x5t1q-e31ae190fad06918677f3dc6f7e80321 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-e31ae190fad06918677f3dc6f7e80321 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-e31ae190fad06918677f3dc6f7e80321 av-styling- avia-align-center  avia-builder-el-10  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45785 avia-img-lazy-loading-not-45785 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/PAICe-Builder-Rod-Pump-Use-Case-banner-1-1-1030x361.png\" alt='' title='PAICe-Builder-Rod-Pump-Use-Case-banner-'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/PAICe-Builder-Rod-Pump-Use-Case-banner-1-1-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/PAICe-Builder-Rod-Pump-Use-Case-banner-1-1-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/PAICe-Builder-Rod-Pump-Use-Case-banner-1-1-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/PAICe-Builder-Rod-Pump-Use-Case-banner-1-1-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/PAICe-Builder-Rod-Pump-Use-Case-banner-1-1.png 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48\">\n#top .hr.hr-invisible.av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48 hr-invisible  avia-builder-el-11  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-tc5sy6-c610ce4ce37ddc8755f7c103673cd0c6 av_one_full  avia-builder-el-12  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035\">\n#top .av_textblock_section.av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Like any manufacturer, upstream oil and gas producers continually seek the next great efficiency improvement. They understand that even fractional gains in core equipment efficiency can yield substantial benefits to production, revenue, and equipment life. For oil producers, sucker rod pumps (SRPs), also known as beam pumps, are under constant scrutiny for this reason.<\/p>\n<p>SRP systems are the most widely used method of artificial lift in onshore oil production. Designed for use on wells that cannot produce the well fluid on their own, which is by far the majority, the systems consist of many surface and subsurface components that must be periodically maintained to optimize operational efficiency.<\/p>\n<p>Sub-optimal production in a single well can cost a producer thousands of dollars per day in lost revenue. Additionally, damaged rod pumping systems require extremely expensive repair processes called workovers, making predictive maintenance essential. A new approach was needed to accelerate efficiency improvements.<\/p>\n<p>Tignis developed a machine learning (ML) model using PAICe Builder to demonstrate how easy it is to apply advanced analytics to automatically detect sudden efficiency losses and emerging equipment issues. The SRP analytics built into Tignis\u2019 PAICe Builder monitor the rod pump cycle, identify anomalies, trigger alerts, and quantify the production losses and impact on revenue on a pump-by-pump basis. The potential impact on the bottom line is considerable.<\/p>\n<p><strong>Performance and diagnostic challenges<\/strong><\/p>\n<p>Any number of pump or rod issues can lead to failure or a drop in SRP process efficiency, such as pump valve leaks, bent rods, wear, corrosion, insufficient liquid supply, fluid buildup, gas interference, and sand production. These failure modes can present themselves in different ways for each pump or failure, making it difficult to write legacy condition-based monitoring alerts.<\/p>\n<p>A dynamometer is one commonly used device to monitor SRP operation. These devices plot the SRP\u2019s rod load versus position through every cycle, which can be compared to an ideal dynamograph to monitor if a pump is behaving normally. Although this method is considered to be very adept at catching SRP issues, the solution can be difficult to implement and maintain. Each SRP\u2019s ideal dynamograph must be tuned individually. Additionally, dynamographs must be re-tuned as the reservoir and well properties, such as crude viscosity or fluid ratios, change over time, resulting in significant maintenance costs.<\/p>\n<p>One proposed methodology of monitoring SRPs is utilizing pure physics-based models of the pump and well to produce the ideal process state and comparing the ideal to real-time sensor data from the well. However, in practice, this solution is not practical. Physics-based models of sucker rod pumps are computationally taxing and cannot be run in real time on currently available hardware. Calculations of a single cycle of the pump using readily available physics-based models can take more than a minute to compute, meaning that the calculation cannot keep up with the incoming real-time data.<\/p>\n<p><strong>Real-time analytics alternative<\/strong><\/p>\n<p>Tignis enables an automated approach to SRP monitoring by providing a tool that allows process\/operations engineers to quickly build real-time ML analytics on physical systems and bring data-driven decision support to challenges across the operation. To provide an example, a new rod pump analytics model was built into PAICe Builder, powered by our proprietary Digital Twin Query Language (DTQL), which helps automate detection and notification of SRP inefficiencies by converting the SRP surface load and position into time-series data and running ML-based predictive models against it in real time.<\/p>\n<p>A key advantage of this method is that ideal operation of the SRP is based on the pump\u2019s historic behavior, meaning the analytic does not require manual setup and tuning for each pump it is applied to. This analysis can be broadly applied to many SRP manufacturers and applications. In addition, the model continues to tune itself over time using new incoming data. This method allows the quick and efficient detection of changes in well efficiency or operation.<\/p>\n<p>Another advantage of using PAICe Builder for SRP analytics is that the embedded machine learning drastically increases calculation times of ideal variables. When trained on historic data, machine learning can predict ideal process states, similar to a physics model, but much more efficiently. This enables monitoring to happen in real time as data is being produced.<\/p>\n<p>Not only does PAICe Builder uncover efficiency anomalies and immediately alert the operations crew, but it also allows engineers to translate the efficiency loss to the loss in barrels per day (bbl\/day) produced, and then translates that oil loss to the loss in potential revenue per day. All this supporting data is included with the alert to help prioritize actions on SRP abnormalities.<\/p>\n<p><strong>Quantifying substantial business value<\/strong><\/p>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t1rotc-98d89de03172ec975defc9f3b4185298\">\n.avia-image-container.av-m8t1rotc-98d89de03172ec975defc9f3b4185298 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8t1rotc-98d89de03172ec975defc9f3b4185298 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8t1rotc-98d89de03172ec975defc9f3b4185298 av-styling- avia-align-center  avia-builder-el-14  el_after_av_textblock  el_before_av_textblock '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45786 avia-img-lazy-loading-not-45786 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/paice-builder-1030x361.png\" alt='' title='paice-builder'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/paice-builder-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/paice-builder-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/paice-builder-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/paice-builder-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/paice-builder.png 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t1kgb9-fca333003d7474f10eaee7c9808c1540\">\n#top .av_textblock_section.av-m8t1kgb9-fca333003d7474f10eaee7c9808c1540 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8t1kgb9-fca333003d7474f10eaee7c9808c1540 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>The SRP analytics model targets conditions that cause insufficient pump rates, such as friction between the pump and other subsurface components, equipment wear, and corrosion. The figure above illustrates how dynamograph data reflects the abrupt change in position and load behavior when a pump\u2019s rate, or strokes per minute (SPM), drops after six minutes of normal operation. Using a simple rule set created in the PAICe Builder app, deviations like this are handled in three steps:<\/p>\n<p>1. Available sensor data, in this case the annular fluid height in the well, flow rate out of the well, and surface pump position, is monitored to detect in real time when a significant change in surface load behavior occurs and its relationship to fluid production behavior, at which point an alert is issued with this information and the following supplemental data points.<\/p>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t1t4j6-d3b0344dec1ee16291e69c18b9c26ed0\">\n.avia-image-container.av-m8t1t4j6-d3b0344dec1ee16291e69c18b9c26ed0 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8t1t4j6-d3b0344dec1ee16291e69c18b9c26ed0 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8t1t4j6-d3b0344dec1ee16291e69c18b9c26ed0 av-styling- avia-align-center  avia-builder-el-16  el_after_av_textblock  el_before_av_textblock '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45787 avia-img-lazy-loading-not-45787 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-08-at-4.41.16-PM-1030x533.png\" alt='' title='Screen-Shot-2021-09-08-at-4.41.16-PM'  height=\"533\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-08-at-4.41.16-PM-1030x533.png 1030w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-08-at-4.41.16-PM-300x155.png 300w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-08-at-4.41.16-PM-768x398.png 768w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-08-at-4.41.16-PM-705x365.png 705w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-08-at-4.41.16-PM.png 1354w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t1kux8-89d52acb5b0e307a4efea11fd49104e5\">\n#top .av_textblock_section.av-m8t1kux8-89d52acb5b0e307a4efea11fd49104e5 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8t1kux8-89d52acb5b0e307a4efea11fd49104e5 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>2. The analytics determine the ideal flow rate (846 bbl\/day) had the anomaly not occurred, as compared to the actual flow rate (506 bbl\/day) as a result of the anomaly, which translates to a production loss of 340 barrels of oil per day.<\/p>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t1tkzm-d3541ac856439146526365f9c1808e82\">\n.avia-image-container.av-m8t1tkzm-d3541ac856439146526365f9c1808e82 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8t1tkzm-d3541ac856439146526365f9c1808e82 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8t1tkzm-d3541ac856439146526365f9c1808e82 av-styling- avia-align-center  avia-builder-el-18  el_after_av_textblock  el_before_av_textblock '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45788 avia-img-lazy-loading-not-45788 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-1030x608.png\" alt='' title='Screen-Shot-2021-09-20-at-10.21.24-AM'  height=\"608\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-1030x608.png 1030w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-300x177.png 300w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-768x453.png 768w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-1536x906.png 1536w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-1500x885.png 1500w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM-705x416.png 705w, \/wp-content\/uploads\/2025\/03\/Screen-Shot-2021-09-20-at-10.21.24-AM.png 1785w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t1l94l-7c80a7884e98f7bfbd9858c075f0f928\">\n#top .av_textblock_section.av-m8t1l94l-7c80a7884e98f7bfbd9858c075f0f928 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8t1l94l-7c80a7884e98f7bfbd9858c075f0f928 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>3. The resultant daily revenue loss is computed by multiplying the production loss amount by oil price data that is either manually input or streamed into PAICe Builder. In this case, at $60 a barrel, the theoretical 40% loss in pump efficiency equates to $20,400 of lost revenue per day.<\/p>\n<p>Armed with this knowledge, corrective actions can be prioritized based on their value to the operation. Simple connectivity to data visualization tools such as OSIsoft\u2019s PI Vision allows operators to rank order the rod pumps by those causing the most immediate or substantial revenue loss, allowing them to concentrate their efforts to maximize production and profitability.<\/p>\n<p>From this example, it is easy to see how early detection of efficiency anomalies can save thousands of dollars per day. Additionally, the SRP analytics reveal issues that don\u2019t immediately impact oil production but can shorten pump life or cause expensive repairs, allowing predictive maintenance to occur before failure. One such example is a pump-off condition where the pump fills with insufficient fluid during upstroke. This condition can lead to fluid pound, causing accelerated stress and fatigue of subsurface SRP equipment, ultimately leading to a premature need to perform an expensive workover of the pump and other subsurface equipment.<\/p>\n<p>The simplicity of the solution is also evident in how the analytics are applicable to a broad range of rod pumps. The custom ML model can be deployed to any rod pump that has sensor data, regardless of its manufacturer, rating, or geographic location.<\/p>\n<p>The SRP model is just one example of how, using PAICe Builder, customers can quickly build a wide range of analytics for themselves to address whatever process challenges they are facing. ML analytics use cases like this one make it clear why artificial intelligence (AI) is the future of process control<\/p>\n<\/div><\/section><\/p><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":45785,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45784","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thought-leadership"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PAICe Builder Use Case - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"An ML model to demonstrate how easy it is to apply advanced analytics to automatically detect sudden efficiency losses and emerging equipment issues.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cohu.com\/tignis\/paice-builder-use-case\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PAICe Builder Use Case - 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