Maillance is a global industrial AI Software-as-a-Service (SaaS) company supporting the full-scale digital transformation of the oil & gas upstream. Its core solution hub oilfield.ai enables companies to make accurate and fast decisions in the domains of production optimization, field development, and reservoir management. At a moment the industry faces huge cost and environmental challenges, Maillance provides hybrid tools combining physics and AI/ML that reduce cost per barrel produced, lower emissions and carbon footprint from operations, at no or minimal investment. Our interdisciplinary team is made of geologists, data scientists, petroleum engineers, and software developers having decades of experience in the O&G industry within major IOCs and Oil Field Services companies. From measurement to prediction, oilfield.ai provides modern predictive business applications to a wide range of oilfield industry challenges, from automated deep learning for seismic processing and interpretation, to production profile forecasting under uncertainty." title="" class="btn" data-container="body" data-html="true" data-id="208812" data-placement="top" data-toggle="popover" data-trigger="focus" style="color:#0077b5" tabindex="0" data-original-title="Maillance"> 656
Activities
Technologies
Entity types
Location
55 Rue La Boétie, 75008 Paris, France
Paris
France
Employees
Scale: 11-50
Estimated: 7
SIREN
833980857Engaged corporates
1Added in Motherbase
1 year, 8 months agoOn a mission to empower oil & gas companies to optimize production, reduce lift cost, and minimize carbon footprint.
Maillance is a global industrial AI Software-as-a-Service (SaaS) company supporting the full-scale digital transformation of the oil & gas upstream. Its core solution hub oilfield.ai enables companies to make accurate and fast decisions in the domains of production optimization, field development, and reservoir management. At a moment the industry faces huge cost and environmental challenges, Maillance provides hybrid tools combining physics and AI/ML that reduce cost per barrel produced, lower emissions and carbon footprint from operations, at no or minimal investment.
Our interdisciplinary team is made of geologists, data scientists, petroleum engineers, and software developers having decades of experience in the O&G industry within major IOCs and Oil Field Services companies.
From measurement to prediction, oilfield.ai provides modern predictive business applications to a wide range of oilfield industry challenges, from automated deep learning for seismic processing and interpretation, to production profile forecasting under uncertainty.
Artificial Intelligence, Petrotechnical Software, Machine Learning, Deep Learning, Data Mining, Visualization, Data Analytics, Optimization, Automation, Data integration, Production Optimization, Oil & Gas, Petroleum Engineering, Geoscience, Reservoir, Emission reduction, and Energy Saving
On a mission to empower oil & gas companies to optimize production, reduce lift cost, and minimize carbon footprint.
Maillance is a global industrial AI Software-as-a-Service (SaaS) company supporting the full-scale digital transformation of the oil & gas upstream. Its core solution hub oilfield.ai enables companies to make accurate and fast decisions in the domains of production optimization, field development, and reservoir management. At a moment the industry faces huge cost and environmental challenges, Maillance provides hybrid tools combining physics and AI/ML that reduce cost per barrel produced, lower emissions and carbon footprint from operations, at no or minimal investment.
Our interdisciplinary team is made of geologists, data scientists, petroleum engineers, and software developers having decades of experience in the O&G industry within major IOCs and Oil Field Services companies.
From measurement to prediction, oilfield.ai provides modern predictive business applications to a wide range of oilfield industry challenges, from automated deep learning for seismic processing and interpretation, to production profile forecasting under uncertainty.