Marco, tell us something about your background, what all you did, where you have worked and what you are doing now?
I started undertaking geophysics as a graduate student at the University of Calgary in 1999. I had just finished my undergraduate degree in Physics at McGill University and had no idea what a seismic trace was. Two years later, in September of 2001, I started my first job at PanCanadian Petroleum after accepting a job offer from Dave Cooper. I started off at the COE, in a two-year training program learning about processing from Anne O’Byrne and seismic attributes from Bill Goodway. It was the perfect situation for me. Nine months later, a merger created Encana and I was lucky enough to stay with the same group of people in the Seismic Analysis Group. After a couple of years there I was finally moved to a business unit, learning about the practical applications of geophysics from John Parkin. I was able to connect the science I learned during my first few years to solving business problems, or at least try to! I moved to Apache in 2006 where I worked in the New Venture Exploration team. After about 3 years there I ended up in the Exploration Production Technology group, reporting to Houston and working on local and international projects. I was able to see data from the North Sea and Argentina, while working the Horn River and Liard basins in Canada. In 2016, I accepted an offer from Velvet Energy and joined an exciting opportunity where I bring all the experience I’ve accumulated to provide seismic value as part of a great geoscience team.
What inspired you to switch from physics to geophysics and take it up as a career?
I was finishing my degree in physics and I didn’t know what I wanted to do after I graduated. I knew I liked working with people and being part of a team, something that I envisioned would be lacking pursuing a career in physics. My dad suggested geophysics – I had no idea what that actually entailed, but I took his advice and applied for Master’s programs at the university of Calgary, Edmonton and British Columbia. I chose to enter the CREWES program and finished my thesis on anisotropic traveltime tomography.
During your building years who were your mentors and why did you admire them?
I’ve had many mentors over my career, people I’ve learned from across different disciplines. Bill Goodway was an inspiration to me, showing the importance of keeping up with technology and the professional fulfillment of pursuing geophysical excellence. Working with John Parkin helped me understand that all the fancy science in the world has little value if there is no business value. I’ve spent a lot of my career working with Greg Purdue, and besides being a friend, his attention to detail and ‘can do’ attitude was motivational. Working for Rob Spitzer taught me to strive for more, not accept the status quo and try what others would not. Doing what everyone else does won’t move the needle or provide a competitive advantage.
What are your aspirations for the future?
To show that geophysics does have value in the unconventional world. I’ve seen in my career geophysics go from being indispensable (as it still is for offshore exploration) to non-existent in some companies developing onshore unconventional resources. The game has changed and the role that geophysics plays isn’t the same, but there still is value in having a geophysicist as part of a development and exploration team. Geophysicists need to adapt from the conventional workflow to the unconventional, and I believe we are behind the engineers in this respect, but it is something we can still strive to achieve. I believe that geophysicists are best suited to integrate most of the data acquired, ranging from geology to geomechanics to fracture mechanics and of course, subsurface estimates of elastic properties.
Apart from AVO/rock physics, what other areas in geophysics do you like and what are your contributions there?
I like problem solving by integrating data, as many different data sets as possible. Sometimes that leads to learning new subsets of geophysics like microseismic, geomechanics and engineering (reservoir and completions). I think my contributions are focused on integration. At Velvet, I can get my hands on a much wider variety of data than I never had before. The unconventional problem is a multivariable one, and looking to solve the problem through the lens of geophysics alone is a losing proposition. I hope to demonstrate how geophysics plays an important role and is required to produce the best workflows.
In the past we have talked about AVO as an anomaly identification technique. More recently attempts have been made for inversion to elastic parameters. How confident are we in actually inverting for elastic parameters and getting meaningful estimates out of that?
I think we can be quite confident in inverting for elastic parameters. The issue, in my mind, is the scale at which we can be confident. Do I know my estimate for impedance is accurate to within 10%? Does my accuracy change as the thickness of the interval changes? And, to what level of precision do I need the estimate of impedance to be before I can extract value? From a value perspective, accurate inversions are only part of the solution. There is also a need for accurate interpretations of the impedances. I think that geophysicists need to learn what a good inversion workflow looks like, and what the final inversion product should look like. Inversions require much more QC and careful analysis and iteration after iteration to get a useable product.
There is a lot of discussion in our industry about getting reliable density information from AVO analysis, whether determined using the vertical component of seismic data, or by way of joint inversion of multicomponent seismic data. What is your opinion on this potentially controversial claim?
I would suggest that of the three properties that one can extract from seismic data through inversion, density is the least reliable. A combination of data quality, the acquisition constraints (lack of far angles) and just the physics of the equation makes it difficult to estimate reliably. That being said, I have seen successful attempts of density estimates with joint inversion and neural networks. Accuracy will always be the issue; it will always be less accurate than the better posed problem of estimating P and S impedance.
Talking about the future of AVO, not very many practitioners have tried the real 3D AVO, wherein you would consider source-receiver paths in different azimuths. One dampening factor here could be the knowledge of velocity anisotropy in the horizontal plane. What are your thoughts on this?
Most of the data I’ve worked with wouldn’t allow for such methods and I haven’t tried or seen results. At Apache, inversions on depth migrated data showed improvements, even on unstructured data from Saskatchewan. The improvements are subtle, yet significant when chasing 10% variations in lithology or 1-2% variations in porosity. This result left me with a mindset that encourages pursuing improved techniques for better and better estimates of subsurface properties. This isn’t to say that older seismic data isn’t useful. There are many examples of reservoir characterization workflow with substandard sampling by today’s standards that is quite successful. I look forward to seeing how successful modern techniques can be!
Rock physics templates (RPT) were introduced some years ago as an aid in doing quantitative interpretation for reservoir characterization. You have carried out a body of work in this area, focused more on unconventional resource characterization. Tell us about the promise of RPTs and how much work is practically being done by employing them in the analysis.
A rock physics template is the link between the seismic inversion results and the reservoir properties of interest to the rest of the geoscience and engineering teams. It brings meaning to elastic properties beyond the estimate of the contrast which produces the recorded reflection. For unconventionals, it becomes more important to develop an RPT. With subtle reflections and minor variations in porosity and lithology, understanding the elastic property variation is vital to mapping production variability using seismic data. In the conventional world, a drilling target is identified as amplitude anomalous from background, hopefully conformable with structural highs. For such drilling prospects, inversion and rock physics workflow can be performed to ascertain reservoir properties. Reservoir properties of interest here are porosity and fluid type. Identifying presence of hydrocarbon is key. The best-case scenario for fluid identification is actually the identifying of a flat spot, or better yet two flatspots. This is not the case in the unconventional world where the reservoir is the background amplitude trend. AVO types were designed for anomalous reflection characterization in the Gulf of Mexico. In unconventionals, AVO loses that utility. Accurate inversions along with rock physics templates are the only chance for seismic to map sweet spots. When trying to map TOC, or brittleness or minimum principal stress, they are related to in-situ subsurface variations of elastic properties. The accuracy of these estimates is vital to be predictive in the only arena that matters – production. Because small variations in geology produce such large variations in production, accuracy is paramount. I believe that the elastic attributes can be related to production variations, but the correlation becomes much stronger when related to reservoir properties. That is why having rock physics templates are important for unconventionals and I’ve had three different areas of exploration to demonstrate that this is so.
These days there is much talk about machine learning applications for seismic facies recognition. Such attempts when calibrated or carried out with the use of rock physics parameters could yield more accurate predictions. Tell us what kind of efforts are underway in this direction.
I believe that the growth area and application for machine learning is to relate inversion and rock physics outputs to production. There are physics models, rock physic models, that relate inversions to reservoir properties. These are derived from physical principles. To my knowledge there is no analytical formulation to relate in-situ reservoir properties to post fracture production. Because of this, I believe the tool of machine learning can provide the elusive link. Not only that, but it can take a wide variety of seemingly unrelated variables and understand the underlying relationships. It comes with the potential of relying too much on the tool and trusting the outputs without considering the actual mechanisms. Facies identification could also be estimated through machine learning, I suppose anything could, I just find there are greater applications elsewhere.
Integrating the results at well log scale with seismic to understand the variability of rock quality that can be correlated with engineering data, with regard to production is a big challenge. Could you comment on this and how you go about doing it?
Estimating production variability is a great challenge. And despite our increasing ability to estimate subsurface reservoir properties, our ability to correlate these to production is still lagging. I think its important to point out that there is a difference between the estimation of static (in-situ reservoir properties) and dynamic (production) properties. I think the expected quality of the correlation is a bit unrealistic. I think low correlation numbers are often interpreted to mean that there is no dependency on the parameters in question. I believe the mindset should be one where if geoscientists can improve production by 10% it has more than proven its value. The strength of correlation to make a 10% difference isn’t that great (over enough samples). Currently I use multivariable regression methods using reservoir and completion parameters.
As you put it, the latest trend in doing so is using real time microseismic data to assess the link between the static in-situ reservoir characterization and the dynamic aspect of reservoir production. Could you please elaborate on this, as to how all this being done at your company?
I find that there is a disconnect between the static and dynamic portions of reservoirs that wasn’t as much of an issue within the conventional realm. Our lack of understanding of what hydraulic fracturing does to the reservoir is still an obstacle to predicting production profiles. We can model completion efforts with a variety of commercial software packages but without some calibration via microseismic data the outcomes are non-unique. Microseismic data is a glimpse into the reservoirs reaction to hydraulic stimulation. The location and magnitudes of the microseisms (along with other attributes) give us hints as to what has been effectively stimulated. There is much debate as to what events are propped or what proportion of events can be related to stimulated rock volume (SRV), but its at least a conversation that can be had.
At Velvet, the workflow consists of integrating microseismic data with reservoir property attributes from inversion to calibrate hydraulic stimulation modelling. When there is a reasonable match, the hope is that the production simulation matches the actual production. When there is a mismatch, I can go back to the microseismic data, and filter events to change what we consider the SRV to look like and rerun the stimulation and production simulation until there is a convergence. It still is a work in progress, but we are getting some increased understanding of the subsurface.
How do you think the recent downturn, which has been dragging for too long now, has impacted the work you and your team members have been doing at Velvet Energy? Is there hope to get out of it this year?
I’m not sure when the downturn will end. The “surface” problems in the Canadian oil industry are more complicated than what I deal with in the subsurface. What this downturn means to me is that geophysics needs to become a leader in technology within the industry. It needs to demonstrate that it is an indispensable tool for economic hydrocarbon production. My hope is that geophysics becomes an integral part of the development and exploration team so that when the downturn does end, we will be ready to contribute.
What are your other interests?
My other interests mainly include staying active, through various forms of exercise. I now enjoy seeing my children learning how to play the sports I played growing up. I follow the Oilers and Blue Jays, mostly ending up in disappointment. I enjoy going to movies and standup comedy. I do enjoy reading books on how humans make decisions and the logical fallacies we commit over-and-over again.
What message would you have for fresh graduates entering our industry?
The message I would have is to stay on the leading edge of geophysics while increasing cross disciplinary knowledge. Falling into the trap of following a software’s prescribed workflow won’t necessarily provide the solutions you are looking for. Learning the latest methods or techniques allows you to break away from what everyone else is doing. It leaves you with an adaptable mind and the ability to look at problems from many different perspectives. Gone are the days of horizon picking and time slices. The modern geophysicist now needs to know more than just 2D and 3D seismic. Geomechanics, completions, microseismic and DAS are all pieces of information that you should be comfortable with. It’s about integration, and you can only integrate what you know.
Finally, what elusive question would you have asked Marco Perez, if you were in my place?
Why did it take you so long to complete this interview? Satinder has been a patient man and I thank him for being gracious in his dealings with me.