DL Vassilios C Kelessidis

Distinguished Lecturer Program
Vassilios Kelessidis

El pasado miércoles 18 de septiembre del año en curso en las instalaciones del Centro Técnico Administrativo de PEMEX en la ciudad de Villahermosa, Tabasco la SPE Sección México recibió al primer conferencista distinguido del tour 2019-2020, al conferencista Vassilios Kelessidis quien presento el tema “Drilling Optimization Revisited: How close are we to Drilling optimization while drilling (DOWD)?

El evento registro una asistencia de 30 personas de manera presencial y 70 equipos conectados, ya sea en equipo personal o en sala de reuniones.

Una vez concluida la presentación el conferencista distinguido respondió una serie de preguntas de los asistentes, al finalizar las preguntas el secretario de la SPE Sección México el M. en I. Edgar Meza Pérez entregó un reconocimiento a Vassilios Kelessidis en nombre de la mesa directiva y la sección, agradeciendo su participación en el tour 2019-2020 el cual siempre busca el beneficio técnico para nuestros miembros.


Vassilios C. Kelessidis is currently a consultant of drilling engineering. He has worked in Schlumberger for more than 9 years and has served later the academia for 18 years: His last position was Professor & Department Chair at Khalifa University (UAE)  (January 2017-December 2018), and Adjunct Professor at Texas A&M at Qatar (TAMUQ). Previously he was at TAMUQ (2012-2016) and at Technical University of Crete, Greece (2000-2016).

He works on drilling engineering, drilling optimization, rock-bit interaction, evolving the drilling predictive simulator, and on cuttings transport, multiphase flows, drilling hydraulics, drilling fluid development and rheology.

He has published more than 115 journal and conference papers with 1640+ citations. He is co-author in SPE Fundamentals of Drilling Engineering, Ch5 - Drilling Hydraulics. He is Associate Editor in SPE Drilling & Completion since 2016.

He has PhD (University of Houston, 1985), M.Sc. (Oregon State University, 1982) and Diploma (Aristotle University of Thessaloniki, Greece, 1980), all in Chemical Engineering.


Drilling engineers and companies strive towards drilling optimization since the era of drilling, with continuous improvements over the years. Drilling engineers generate big data during drilling campaigns but significant information is hidden.

Big Data Analytics and the much-improved Artificial Intelligence and Machine Learning techniques provide excellent opportunities for enhancements of drilling optimization. Now data could be exploited very intelligently and ‘on the fly’. However, all this must be done cautiously and always in combination with appropriately developed physical models to maximize benefits.

For drilling optimization, physical modeling is needed using data to calibrate and tune the models rather than using only data driven models without physics.

We focus on rock-bit interaction as the most significant area for drilling optimization. Physical modeling exploits monitored data and generates responses of the bit. The drilling simulator is tuned to match actual drilling data. Sonic while drilling data provide estimates of rock strength on the fly. An optimization scheme can estimate drilling parameters on the fly for drilling the next segment and communicates them to the driller, giving him the capability to ‘ride’ the bit and guide it safely to target.

Take away Idea: Big data analytics and right modelling can help put the driller on the bit.

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