Petroleum Engineering Distinguished Seminar Series

The Petroleum Engineering Distinguished Seminar Series brings in innovators and leaders from industry and acaedmia. Seminars are bi-weekly, Mondays at 4 pm.  Beginning on 1/25/2021 and ending on 4/19/2021.

Add to your calendar so you don’t miss one: ZOOM Seminar Series

 

Spring 2021 Semester Speakers

January 25, 2021: Ahmed Amer, Newpark Fluids Systems

January 25, 2021: Ahmed Amer, Newpark Fluids Systems

New R&D Trends to address Lost Circulation

4 pm MST via Zoom

Lost circulation remains a challenge in mature depleted fields as well as exploratory prospects. While the challenge is the same, the underlying causes are different where there is an abundance of rock properties data in mature fields and scarcity of data in exploratory ones.

The evolution of “Engineering Solutions” such as MPD among other technologies like “Casing Drilling” has been significant. That being said, fluid design in these applications remains critical to the success of the entire operation.

A significant challenge to lost circulation prevention and mitigation is selecting the appropriate solutions from those available on the market. Rather than a shortage of such products, the industry suffers from an overabundance of lost circulation materials (LCM); the majority of which are variations of a few non-innovative types.

Current research and development efforts should focus on  novel solutions that deliver results in the field.  A solution-based categorization approach that integrates loss rates and loss mechanisms and links them to the solution having the highest probability of success.

Check out the full bio and abstract!

FEBRUARY 8, 2021: DR. HARIPRASAD JANAKIRAM SUBRAMANI, CHEVRON

FEBRUARY 8, 2021: DR. HARIPRASAD JANAKIRAM SUBRAMANI, CHEVRON

Current trends in Flow Assurance: from R&D to Applications

4 pm MST via Zoom

The term flow assurance appears to have come into existence in the 1990s when the industry was pushing into harsher environments such as deep water. It was a realization that the industry would need to be able to guarantee flow over longer distances, 24/7 with high reliability, in a high-stakes environment where intervention to rectify issues and blockages would be costly. Risk avoidance as a result was the default flow assurance strategy. We are about 20+ years in with the field of flow assurance.  Today, deep water and harsher environments are still a part of the portfolio of assets, but there is a need to make those assets thrive in a prolonged lower oil price- / lower carbon- environment and compete for limited investment capital.  Some of the current trends in Flow Assurance from R&D technology gaps to practices in field applications will be covered in this presentation.

February 22, 2021: Dr. Roland Horne, Stanford

February 22, 2021: Dr. Roland Horne, Stanford

Big Data and Machine Learning in Reservoir Analysis

4 pm MST via Zoom

Well monitoring can provide a continuous record of flow rate and pressure, which gives us rich information about the reservoir and makes well data a valuable source for reservoir analysis. Recently, it has been shown that machine learning is a promising tool to interpret well transient data. Such methods can be used to denoise and deconvolve the pressure signal efficiently and recover the full reservoir behavior. The machine learning framework has also been extended to multiwell testing and flow rate reconstruction.

Multiwell data can be formulated into machine learning algorithms using a feature-coefficient-target model. The reservoir model can then be revealed by predicting the pressure corresponding to a simple rate history with the trained model.

Flow rate reconstruction aims at estimating any missing flow rate history by using available pressure history. This is a very useful capability in practical applications in which individual well rates are not recorded continuously. The success of rate reconstruction modeling also illustrates the adaptability of machine learning to different kinds of reservoir modeling, by adjusting features and targets.

Machine learning is also a particularly promising technique for analysis of data from permanent downhole gauges (PDG), given that the massive volumes of data are otherwise hard to interpret using conventional interpretation methodologies.

March 8, 2021: Dr. Ding Zhu, Texas A&M

March 8, 2021: Dr. Ding Zhu, Texas A&M

Title TBA

March 22, 2021: Dr. Jingjun (JZ) Zhang, ConocoPhillips

March 22, 2021: Dr. Jingjun (JZ) Zhang, ConocoPhillips

Title TBA

April 5, 2021: Mr. Kyle Haustyeit, Devon Energy

April 5, 2021: Mr. Kyle Haustyeit, Devon Energy

Title TBA

April 19, 2021: Dr. Abbas Firoozabadi, Yale

April 19, 2021: Dr. Abbas Firoozabadi, Yale

Title TBA