• Excise the word “like” from your spoken English.
• Don’t suck up, meaning don’t flatter your supervisors.
• Stop “reaching out” and “sharing.”
• Rid yourself of piercings, tattoos, and weird hair colors.
• Make strong language count.

As a father I thought all of this was good, solid advice.

With the Curmudgeon in mind, I have a few comments to make about our life as researchers, pushing the leading edge of technology in our industries.  I think one of the very important technologies in the past decade that John Sherwood pioneered is Beam Technology: Fast Beam Migration, Gaussian Beam Migration and Beam Tomography.  Saudi Aramco just published an article in the August issue of The Leading Edge (TLE) describing the use of Fast Beam Migration combined with Beam Tomography for track processing, to obtain a depth stack and high-resolution velocity mode in three days after acquisition. I think that is very impressive, and I believe no other company in the world can match that speed.  From the paper:

In terms of fast turnaround time, the following indicative field productivity metrics were achieved: for a partial block of acquisition 100 km2, using 38,000 recording channels, 24 vibrators were utilized, producing an average of 50,000 sweeps per 24-hour day of operations and a duration of three days recording 30 million traces per square kilometer.

Moreover, it took three days for fast-track processing, including FBM tomography producing image gathers, of this partial volume using 200 CPU nodes (dual 20 cores, 384 GB RAM per node).  Therefore, in six days we have acquired and processed 100 km2 generating a multiplicity of velocity-depth models and their corresponding depth images and associated image gathers while the seismic crew was still acquiring data in the vicinity (Tsingas et al., 2020).

I recently gave a one-day class on Advanced Depth Imaging, part of the Geophysical Society of Houston (GSH) in-person Geophysics Academy and had to explain Smart Migrations (Beams) vs. Dumb Migrations (Kirchhoff or RTM).  My analogy was imagining you are a soldier in a trench, shooting at the enemy.  In Kirchhoff migration you don’t know where the enemy is so you shoot 180 degrees, while in Smart Migrations, you measure the dip and azimuth of the incoming fire and shoot back at the location of the enemy.  Here is my class slide explaining how not to waste bullets in migration:

In my classification Beam Migrations are a subset of Smart Migrations, a class of algorithms that use information in the prestack input data to guide the migration operator, in contrast with Brute Force Migrations (or Dumb Migrations) such as Kirchhoff or RTM that make the assumption that every point in the subsurface is a diffractor, do not use the stack information available and make no a priori assumptions about the migrated image structure.  There are some hybrid algorithms that use the dips from the stack to constrain the aperture, that do not fit neatly in my classification, but the helicopter view still stands. 

The other application for Beam Migration is to obtain Full Waveform Inversion (FWI) like velocity models with 10-15 m lateral resolution, 100 times faster than standard FWI, which makes the technology usable by smaller companies that cannot afford 100,000 CPUs, but that will be in my next column.

References: From field to office – A fast-track beam depth migration with tomography updates applied to blended data, Authors: Ali Alzayer, Constantine Tsingas, Nick Tanushev, Mihai Popovici, and Mohammed Almubarak, The

The Cecil Green Enterprise Award was established to recognize the importance of an individual enterprise to the economic vitality of our industry and shall be conferred from time to time on persons who have demonstrated courage, ingenuity, and achievement while risking their own resources and future in developing a product, service, organization, or activity which is recognized as a distinct and worthy contribution to the industry.

Advanced Depth Imaging Agenda

Alexander Mihai Popovici, CEO, Z-Terra Inc.

Velocity Model Building

Main concepts and the evolution over time of migration velocity analysis (MVA) and tomography.

Automatic PSTM velocity model computation methods .

1. Beam Forming for automatic Vrms generation.
2. AutoImager by Data Modeling Inc.
3. Kinematic Invariant Velocity Update (KIVU).

PSDM velocity model, tomography and evolving the velocity model from time to depth.

1. Migration Velocity Analysis (MVA) and fundamentals of updating the velocity model and iterative MVA.
2. Wave-equation MVA and angle gathers.
3. Reflection Tomography.  Horizon based tomography.  Grid based tomography.
   1. Single value tomography.
   2. Multiple value tomography (MVT).
   3. Azimuth sectors and MVT.
   4. Extended Gathers.
   5. Beam Tomography.
4. Full Waveform Inversion (FWI).
5. Beam Tomography as a faster and more stable alternative to FWI.

Effect of rugosities on top structures for imaging complex structures.  Illumination effects.

Building velocity models with complex salt bodies.

Imaging Methods

Prestack depth migration and prestack time migration fundamentals.

Wave-equation depth migration improvements, theory, image comparisons.

• Kirchhoff.
• Common Azimuth migration, Shot Profile migration.
• Reverse Time Migration.
• Gaussian Beam Migration, Fast Beam Migration.
• 5-D and 6-D Data Regularization.
• Wave Equation Multiples Imaging.

Fast Beam Migration.  Beam Forming by Slant Stack.  Beam Forming by Plane Wave Destructor (PWD) filters.  Image Forming by Beam Migration and Reconstruction.

Short Review of Beam Forming and Beam Migration:

1. Data Decomposition via Beam Forming.
2. Migration and Image Reconstruction.
3. Fast Beam Migration implementation and examples.
4. Gaussian Beam Migration implementation and examples.

Results, synthetic, real data.

Anisotropy effects in depth migration.  Imaging in the presence of anisotropy and evaluating the anisotropy.

Broadband Diffraction Imaging

Diffraction Imaging (DI) is a high-resolution imaging technology designed to image and identify in very fine detail the small scale fractures in shale and carbonate reservoirs that form areas of increased natural fracture density.  Diffraction Imaging provides a separate 3D (stack), 4D (angle gathers) or 5D (angle and azimuth gathers) image of discontinuities, or objects which are small compared to the wavelength of seismic waves such as fault edges, small scale faults, fractured zones, pinch-outs, reef edges, channel edges, salt flanks, reflector unconformities, injectites, fluid fronts, caves and karst, in general any small scattering objects. 

Overview of Diffraction Imaging (DI)

1. Diffraction and scattering theory.
2. Specular reflections vs. diffractions.
3. Diffraction imaging benefits; super-resolution.
4. DI implementation in Kirchhoff time and depth migration.
5. Pre-processing pitfalls in removing diffractions.

Diffraction imaging examples and comparisons.

1. Synthetic examples.
2. Real data examples. 
3. Amplitude with azimuth. 
   1. Direction of stress fields.
   1. Visualize azimuthal dependence.
4. Correlation of diffraction imaging attributes with production.

I decided to revisit the numbers in 2024, in the November 2018 article I had to estimate the revenues for 2018 as the data from the public companies was only available for the first two quarters, so I was curious to see how my estimate held against the real numbers.  The approximation was pretty good for most of the companies, with a few others doing better, as you can see from comparing the old table with estimates with the new table with current numbers.  Some companies restated their revenues post 2018, you will also find that comparing the two tables.

My observation (and hope) that in 2018 “We are starting to pick ourselves up as an industry.” was partially correct.  The downturn started in 2014 and bottomed out in the middle of 2016.  Then the industry revenues started to grow from the 2016 bottom through 2018 and 2019, then in 2020 Covid hit and the industry revenues took another dip.  I was curious to learn if the second dip was lower or higher than the bottom in 2017.  It turned out that it was about the same combined revenue value.   

Table 2:  Service companies revenues from 2012 to 2023 (in USD millions.  In yellow the peak revenue number for each company.

Looking at revenues of several publicly traded service companies (Table 2) over the past twelve years, it seems that on average the service industry took about a 60% hit from the highs in 2013 and 2014 to the low in 2016, as you can see in Figures 1 and 2.  CGG was down from the peak of $3.768 billion in 2013 to $1.197 billion in 2016, a 68% drop.  PGS was down from a $1.518 billion peak in 2012 to $764 million in 2016, a 50% drop.  ION went from $549.17 million in 2013 to $172.81 million in 2016, a 69% drop.  TGS went from $914.78 million in 2014 to $455.99 million in 2016, a 45% drop.  Schlumberger went from $48.87 billion in 2014 to $28.01 billion in 2016, a 43% drop.  Missing from the 2023 numbers is the ION revenue, which went bankrupt in 2022. 

What can we learn from the numbers?  It seems Baker Hughes and TGS did the best compared to their peers, their revenue recovered to 89.8% and 81.2% from their peak in 2013 and 2012 respectively.  Then there is a second group of companies, Halliburton, SLB, PGS, Core Labs that recovered to 50-60% from the peak, and a third group that did not do that well, CGG, Weatherford, NOV.

And lastly Dawson and ION, at the bottom of the performance ranking table.  Dawson was acquired by the Wilks Brothers, and ION went bankrupt in 2022.  CGG (now Viridien)  seems to be in trouble if you look at Figure 3, they have the lowest recovery-from-the-peak numbers.

How about the future?  I was optimistic in 2018, I thought the industry was on a path to recovery, then the Covid lockdowns hit and the industry went downhill.  Again.  Barring some catastrophic event that I cannot predict, I think the industry will do well the next few years.  Will it get back to the peak of 2014?  I don’t know.  I hope so, but we lost a lot of good people.  Maybe some will come out of retirement and maybe we’ll get more students interested in the field as the job demand grows. 

Figure 1:  Total revenues from 2012 to 2023 for several service companies.  2023 revenues were estimated based on summing the last four quarters, which likely is a lower number than the final one. 

Figure 3:  Normalized service companies revenues from 2012 to 2023.

When I became President Elect of the Geophysical Society of Houston (GSH) I did the same analysis of the GSH revenues over the last 10 years.  It turns out there is a very high correlation between society’s revenues and the industry revenues.  The society had peak revenues in 2014, then a big drop in the downturn 2014-2017, then a small recovery, then a second drop in the Covid pandemic, and now it is going up like the rest of the industry.  I am optimistic about the future, I think better times are ahead of us as an industry and for GSH as well.

Left: the old Honeywell thermostat.  Right: the Nest thermostat is an example of High-Tech Low-Tech.

The co-founders of the Nest company were Apple employees who worked on developing the Ipod and Iphone.  They left Apple to start the company making the Nest thermostat that eventually was bought by Google for a billion dollars.  Before Nest, thermostats looked like the Honeywell in the left figure, cheap looking white plastic boxes with an outdated look and a small display screen.  Perfect example of Low-Tech.  The Nest thermostat has a beautiful minimalist design, blending smartly form and function.  The first thing that you enter after you connect it with the wall wires is the home wireless Internet password.  The thermostat then gets the outside temperature based on your location, the average historical high and low temperatures in your area and starts optimizing your thermostat operation based on your daily habits and the outside temperature.  It uses motion sensing so when you walk past it the screen automatically lights up to show the temperature and time.  It allows you to see the temperature in your house from your smart phone.  It optimizes via machine learning from your hourly usage the temperature schedule in your house.  The Nest thermostat is an example of High-Tech Low-Tech.  It takes a simple or older technology and combines it with advanced features and advanced functionality.

An old Silicon Valley mantra postulates that money is not in technology but in the business application of technology. 

That gets us back to Beam Technologies and Smart Migrations.  In the same way the Nest thermostat took the old temperature control device to a higher level, Smart Migrations are taking the Brute Force Migrations (or Dumb Migrations) to a new level by incorporating advanced features.  Ray tracing, which underpins beam methods, has been theoretically understood and computationally feasible since the 1960s.  Ray tracing and beams are Low-Tech.  Combining Beams and standard Tomography into Beam Tomography that produces velocity models very similar to the Full Waveform Inversion (FWI) models, is High-Tech Low-Tech.

An important point to remember is that Reverse Time Migration (RTM), FWI and other computationally intensive methods don’t create information to form a detailed image or velocity model — the information must be present in the seismic data.  This is where the first High-Tech feature of beam migrations enters: the seismic data is analyzed for coherent signal and is distilled to a small number of beams that carry the essence of the data.  After this velocity-less procedure is accomplished, migrating the beams with any specific velocity is a question of minutes. The second High-Tech feature is to carefully synchronize migrated beams that image the same seismic structure. The mismatch between the beams is converted to corrections of the velocity field. Since beams propagate through a small tube in the earth, the velocity modifications have pinpoint accuracy and are rich in detail. Iterating fast beam migration and these velocity updates, allow for automated velocity model building that conforms to seismic structures and has details akin to velocities produced by FWI in a faster and much less computationally intensive way.  Why not use all the available information to improve the quality of the image and reduce the computer resources to obtain it? 

PGS had first mover’s advantage when they bought Sherwood’s company, the original developer of Fast Beam Migration, but missed out the opportunity to differentiate their offering based on Beam Technologies and instead joined the herd in pushing RTM and FWI, where they do not have any differentiating product. 

Diffraction Imaging is a high-resolution imaging technology designed to image and identify in very fine detail the small scale fractures in shale and carbonate reservoirs that form areas of increased natural fracture density.  Diffraction Imaging provides a separate 3D (stack), 4D (angle gathers) or 5D (angle and azimuth gathers) image of discontinuities, or objects which are small compared to the wavelength of seismic waves such as fault edges, small scale faults, fractured zones, pinch-outs, reef edges, channel edges, salt flanks, reflector unconformities, injectites, fluid fronts, caves and karst, in general any small scattering objects.  Our Diffraction Imaging implementation works by eliminating the large amplitude specular reflections from the migrated image and preserving the diffraction amplitudes that can be hundreds of time smaller in amplitude than specular reflections.

Figure 1:  Diffraction imaging depth slice in Eagle Ford.  Left PSDM, right DI.  Notice the increased resolution of discontinuities.

I was initially reluctant to work on Diffraction Imaging, because to me it sounded as yet another Coherency attribute, similar to negative or positive curvature, and I didn’t think the industry needs yet another Similarity attribute.  I found out I was wrong and the Aramco researchers were right after we started to use DI on unconventional shales.  First I was wrong because the DI volume is not just an attribute, it is a migration volume with phase and amplitude, and also offset (4D) and azimuth (5D) distribution.  The azimuthal distribution of the DI amplitudes gives information about the direction of the stress field.  Second, a big surprise to me was the resolution of the faults and fractures that we could see in the DI prestack migrated data.  We spent almost a year modeling and DI migrating increasingly smaller discontinuities to understand the limits of resolution of the method.  We created a simple model, a few layers with variable velocity and discontinuities of various lengths in the density model.  Initially we used finite-difference modeling using a fixed acquisition geometry and fixed output image grid and later we switched to Ray-Born modeling as the length of the discontinuities became meters then centimeters and finite difference modeling on a centimeter grid was too computationally expensive.  The conclusion is obvious once I state it, but it took me lots of CPU time to reach it.  We could see in the DI migrated image even the centimeter length discontinuities, since there was no noise in the synthetic, the very small diffracted energy from small fractures and discontinuities gets focused in the output grid, independent of the size of the discontinuity.  Of course, in real life the noise level in the data impacts the visibility of the small amplitude diffractions, but these were synthetics.

The unconventional shales drilling technology was spearheaded in the early 2000s by small companies with expert drilling engineers, without much focus on seismic imaging, reservoir engineering, or field optimization.  The emphasis was on drilling cost and on recovering the drilling cost for each well through individual well production.  As larger companies with reservoir characterization and reservoir optimization teams got involved, the emphasis changed from the cost of individual wells to field optimization.  It was noticed that wells placed very close using a grid pattern could have very different oil and gas production.  The hypothesis was that the structure is very simple and therefore the production should be uniform.  In reality the production is not uniform and two wells placed very close by can have very different production. 

New high-resolution technologies are needed to define and visualize the structure and the natural fracture distribution and orientation in shale layers.  Optimal well placement requires the operator to factor the predominant trend of natural fractures in the selection of the wellbore orientation.  Diffraction Imaging is a new approach to image with super-resolution small scale faults, fractures, reflector unconformities, in general any small scattering objects.  Typically DI is used as a complement to the structural images produced by reflection imaging.  By identifying the areas with increased natural fracture density, the reservoir engineers can design an optimal well placement program that targets the sweet spots, areas with increased production, and minimizes the total number of wells used for a prospective area.  Using an optimal number of wells decreases the drilling cost while maximizing production and decreases the environmental impact of developing the field by using less water, using less sand and chemicals pumped in the well and not disturbing the local communities.

• Excise the word “like” from your spoken English.
• Don’t suck up, meaning don’t flatter your supervisors.
• Stop “reaching out” and “sharing.”
• Rid yourself of piercings, tattoos, and weird hair colors.
• Make strong language count.

As a father I thought all of this is good, solid advice.

With the Curmudgeon in mind, I have a few comments to make about our life as Researchers, pushing the leading edge of technology in our industries.  Herding a small group of developer cats at Z-Terra, I have to point them to research areas in the pasture where the grass is not so trampled by the large companies research groups with larger budgets and lots of smart people working on fashionable topics.  In small companies it helps to be a contrarian, to develop novel algorithms in areas overlooked by large research groups or the academic groups funded by them. 

A few years ago, I read a book by Peter Thiel, “Zero to One.”  He starts the book with a question he always asks people he interviews for a job: “What important truth do very few people agree with you on?”  It is a contrarian question that is related to the title of his book, he argues that while incremental innovations, making existing things better, is going from 1 to n, inventing new technology is going from 0 to 1.  That made me think what would be my answer to his question, and one possible result is Smart Migrations vs. Full Waveform Inversion (FWI) and Reverse Time Migration (RTM).

While a large number of researchers in our industry have joined the stampede on FWI and RTM, high end imaging tools requiring more and more computer resources, fewer have followed John Sherwood’s work on improving Beam Migration and Beam Tomography.  I believe the combination of Fast Beam Migration (FBM) and Beam Tomography can lead to high resolution velocity models similar to the ones obtained using FWI, but 100 to 1000 times faster while at the same time the algorithm is very stable, unlike FWI which easily falls into local minima.  A while back I attended a local Geophysical Society of Houston (GSH) presentation where John Sherwood was showing examples of Beam Tomography results.  One of the results was a velocity model after Beam Tomography where the velocity update was different inside a river channel, somewhere between 1000 and 2000 meters deep.  I was blown away by that resolution, never before did I see the results of a tomography update that had so much detail.  I came back to the office convinced we have to work on this technology.

In my classification Beam Migrations are a subset of Smart Migrations, a class of algorithms that use information in the prestack input data to guide the migration operator, in contrast with Brute Force Migrations (or Dumb Migrations) such as Kirchhoff or RTM that make the assumption that every point in the subsurface is a diffractor, do not use the stack information available and make no a priori assumptions about the migrated image structure.  There are some hybrid algorithms that use the dips from the stack to constrain the aperture, that do not fit neatly in my classification, but the helicopter view still stands.  One early and successful commercial implementation of a Smart Migration class algorithm is the Fast Beam Migration developed by John Sherwood at Applied Geophysical Services.  The speed of FBM is achieved in two steps:

1. A factor of 5-10 in speedup is achieved using beam forming, or beam decomposition of the input data, where the size of the input data is reduced by a factor of 5-10.
2. A factor of 10 to 100 in speedup is obtained by spreading each input trace or beam over a beam instead of a full aperture-volume.

Beam Tomography works by combining standard tomography with Beam Migration.  The industry standard reflection tomography performed in the post-migrated domain has many advantages over standard tomography performed on prestack data.  In general, post-migrated events are much easier to pick, the data volume is more manageable, and the whole process is more robust.  The procedure converts common image gather residual picks to velocity changes using 3D tomographic back-projection.  In tomographic MVA, fans of rays are used to backproject residual velocities to the places where the velocities errors originated.  The state of the art tomography in the early-mid 2000s was based on single value updates, from each (x,y,z) point in the image, a single value for the residual velocity (or time delay) was used to update the velocity along all offset and azimuth rays.  The state of the art tomography in the mid-late 2000s, was based on event picking in offset gathers, and generating residual velocity updates from each (x,y,z) point in the image, resulting in a vector of velocity residual values function of offset dV(h) used to update the velocity.  Since you have 50-100 bins in an offset gather, the number of independent velocity values for each (x,y,z) point is on the order of 50-100.  The state of the art today (2020) is to separate the input data by several main azimuths (typically 5-10), migrate them separately, and update the velocity model using several azimuths and all offsets dV(h;a).  6 azimuths multiplied by 60 offsets generates 360 independent values for the velocity update.  In Beam Tomography about 10,000 source-receiver pairs contribute to the velocity update for each (x,y,z) point in the subsurface.  That will be the state of the art in the industry in 5-10 years from now.

The faster imaging software allows for more automatic iterations of velocity model building (100-500 iterations, instead of the current 7-10), which enable the processing team to enhance the seismic resolution and imaging of complex geologic structures, and allows for deeper data penetration, steeper dip and sub-salt structure imaging.  Improved velocity models in combination with wave-equation imaging provide much greater resolution and accuracy than what can be accomplished today with standard imaging technology. This technology is not yet mainstream in the industry, is a fundamental advance, and is a necessary building block in any seismic processing system that uses wave-equation methods for imaging ultra-deep land and water, complex geological structures, which are the focus of modern oil-and-gas exploration.

On the walls of our Houston Z-Terra office, we have a lot of movie posters.  It’s a tradition that started at 3DGeo when our office was across the road from Silicon Graphics, which made computers used for computer generated imagery and which had a lot of movie posters on its walls.  We visited Silicon Graphics often for support.  We kept the tradition at Z-Terra, and each poster comes from somebody’s favorite movies list.  One of the posters is from Batman Begins and includes a quote from the movie: “Why do we fall, sir? So that we can learn to pick ourselves up.”

We are starting to pick ourselves up as an industry.

Looking at revenues of several publicly traded service companies (Table 1) over the past seven years and estimating 2018 based on numbers from the first two quarters, it seems that on average the service industry took about a 50% hit from the highs in 2013 and 2014 to the low in 2016, as you can see in Figures 1 and 2.  CGG was down from the peak of $3.768 billion in 2013 to $1.197 billion in 2016, a 68% drop.  PGS was down from a $1.518 billion peak in 2012 to $764 million in 2016, a 50% drop.  ION went from $549.17 million in 2013 to $172.81 million in 2016, a 69% drop.  TGS went from $914.78 million in 2014 to $455.99 million in 2016, a 45% drop.  Schlumberger went from $48.87 billion in 2014 to $28.01 billion in 2016, a 43% drop.

The oil price collapse, which began in June 2014, initiated a wave of cost reduction among upstream businesses.  Global oil and gas companies slashed capital expenditures by about 40 percent between 2014 and 2016. As part of this cost-cutting campaign, some 400,000 workers were let go, and major projects that did not meet profitability criteria were either canceled or deferred.  The human cost of restructuring within the oil and gas sector has been enormous. Downsizing, which has been both cyclical and harsh, has deprived the industry of some of its smartest veteran talent while scaring away new recruits.  The wave of worker layoffs eliminated significant experience, knowledge, and skills.  The loss of these capabilities could push development project costs up substantially.

Figure 1:  Normalized service companies revenues from 2012 to 2018.  2018 revenues were estimated based on the first two quarters.

Figure 2:  Total revenues from 2012 to 2018 for several service companies.  2018 revenues were estimated based on the first two quarters.

I used to keep track of layoffs in the industry, different company announcements, friends and colleagues losing their jobs.  I stopped doing it, too depressing.  Compared to the oil-and-gas industry where if you divide the total company revenue by the number of employees you get a number in the $4 million to $5 million range, in the services industry that same number is in the $300K to $400K per employee range.  No wonder when the revenues decrease to half, the first cuts are to people. Salaries are the one of the largest components in service companies cost.

The good news is that 2017 was a year in which all the companies started to recover, albeit a very slow recovery. The bottom appears to have been in the first half of 2016.  It seems the new focus of oil and gas companies has been in becoming more efficient at the cost of investing in new reserves discovery.  In an industry where reserves and production decline naturally as oil is pumped from fields, continued investment in exploration is critical for a company’s market capitalization.  Without the exploration expense, the oil and gas reserves of the world’s top energy companies have steadily declined since 2014.  Right now (in 2018) it seems the large oil companies are increasing reserves by acquisition.  Globally, that is a zero sum game.  Without new exploration, the global reserves will continue to decrease.

In more good news, analysts at Morgan Stanley and Jefferies predict spending by the world’s top seven oil companies will rise to a combined $136 billion by 2020 from $105 billion in 2017.  If those projections hold true and 2020 is indeed a growth year, we simply need to survive in this new lean environment for another year or so.  Growth is on the horizon.

We are working in the largest industry in the world by revenue and market capitalization.  The top 4 oil and gas companies ExxonMobil, Shell, Chevron and BP have a market capitalization of about $1 trillion and annual revenues in the same range.  Yet these 4 companies represent only 18% of the total market, with the rest of 82% distributed among many National Oil Companies (NOC) like Saudi Aramco, Petronas, Petrobras, Pemex, Statoil, Sonangol, Ecopetrol, etc.  The initial public offering (IPO) of Saudi Aramco in 2018 will create a company with a market capitalization in the $1-2 trillion range, the largest IPO in history, making Silicon Valley unicorns look like tiny blips in the market.  This enormous value and value creation industry will not disappear overnight and be replaced by renewables and electric cars, as the media would like you to think.

Rising living standards over the world, particularly in non OECD countries like China and India will move billions of people in the global middle class.  As growth accelerates, so does consumption as more people get access to air-conditioned homes, cooking energy, cars and appliances like refrigerators, dishwashers and laundry washers.  Access to energy is fundamental to improving quality of life and is a key imperative for economic development.  In the developing world, energy poverty is still rife.  Nearly 1.6 billion people still have no access to electricity, according to the International Energy Agency (IEA).

The first part of my answer to the students was that oil and gas products are not only used for energy and transportation; they are the main component in plastics and fertilizers. Also, asphalt and road oil used to build roads, highways, playgrounds, and sidewalks, are made from oil products.  Plastics are produced from natural gas, and feedstocks are derived from natural gas processing and from crude oil refining.  Figure 1 shows the percentage comparison of the different types of energy production: oil, gas, coal, hydro, nuclear and renewables, while Figure 2 shows the size and contribution of each energy type in the four major areas of use:  Transportation, Residential and Commercial, Industrial and Electricity Generation.  Oil and gas remains and will be for the next 50 years the world’s primary energy source.  Nuclear and renewables will grow strongly, with natural gas growing the most.  Solar and wind renewable energy, as you can see in Figure 1, account for only 4% of the global energy mix, and will see tremendous growth in the next 25 years to account for about 11% in 2040. Renewables will have the greatest impact on electricity generation and to a much smaller extent transportation, with almost no impact on residential, commercial and industrial energy use, as you can also see in Figure 2.  The bottom line is that 50 years from now, when the current student generation is retired and will start mentoring the next generation, oil and gas will still be the most important source of energy in the world.

As global economies grow and government policies change, the energy mix will continue to diversify. The diversification of energy supplies reflects economics and advanced technologies as well as policies aimed at reducing emissions.  Natural gas is the largest growing fuel source, providing a quarter of global energy demand by 2040. The abundance and versatility of natural gas is helping the world shift to less carbon intensive energy for electricity generation while also providing an emerging option as a fuel for certain types of transportation.  The world will need to pursue all economic energy sources to keep up with the increase in global population from 7 billion people to 9 billion.  Oil and natural gas will likely be nearly 60 percent of global supplies in 2040, while nuclear energy and renewables will grow about 50 percent and be approaching a 25 percent share of the world’s energy mix.

Figure 1: Historical percentage distribution of different types of energy from 1966 to 2016 (BP Statistical Review of World Energy 2017).

Figure 2: Energy demand by sector 2015-2040 (ExxonMobil 2017 Outlook for Energy: A View to 2040).

The second part of my answer to the students was that electric cars will grow from the current 1.2 million to 100 million in 20 years, but will be only 5% of the total of 2 billion cars.  There are about 1.2 billion cars in the world today, and the number will grow to about 2 billion in 20 years.  In the US there are approximately 800 cars per 1000 people.  In Germany 600, in China about 100 and in India around 20.  As China and India catch up to the car density in the US and Western Europe, a lot more cars will be manufactured, and electric cars will see a tremendous growth, a factor of 100.  But twenty years from now 95% of the cars will still run on gasoline, and the internal combustion engine will also increase in efficiency to keep up with the technology advances in the electrical cars.  And this is just my personal opinion, but I think electric cars are for wusses.  I love the low growling vibration of my 5.5 liter V8 turbocharged Mercedes AMG engine, even though I get less than 20 miles per gallon.

Figure 3:  Distribution by country of cars per 1000 people.

So my advice to the students in summary?  It’s a great industry to work in for the long run.  The salaries are good, you get to travel and see the world, and you can provide energy services to the world.  Business, industry, commerce and public services such as modern healthcare, education and communication are highly dependent on access to energy services.  There is a direct relationship between the absence of adequate energy services and many poverty indicators such as infant mortality, illiteracy, life expectancy and total fertility rate.  And if you want to do even more good in the world, volunteer for Geoscientists without Borders.

I just finished reading a book by Peter Thiel, “Zero to One.” He starts the book with a question he always asks people he interviews for a job: “What important truth do very few people agree with you on?” It is a contrarian question that is related to the title of his book, he argues that while incremental innovations, making existing things better, is going from 1 to n, inventing new technology is going from 0 to 1. That made me think what would be my answer to his question, and one possible result is Smart Migrations vs. Full Waveform Inversion and Reverse Time Migration.

While a large number of researchers in our industry have joined the stampede on Full Waveform Inversion (FWI) and Reverse Time Migration (RTM), high end imaging tools requiring more and more computer resources, fewer have followed John Sherwood’s work on improving Beam Migration and Beam Tomography. I believe the combination of Fast Beam Migration and Beam Tomography can lead to high resolution velocity models similar to the ones obtained using FWI, but 100 to 1000 times faster. I will explain why in more detail later, but first some background on Smart Migrations, a class of algorithms that uses information inside the prestack data to greatly speed up the turnaround time for large 3-D seismic projects.

Smart Migrations are a new generation of depth imaging algorithms, two orders of magnitude faster than the standard Kirchhoff depth migration. These clever algorithms, combined with very fast migration velocity analysis tools, can reduce the turnaround time for large 3-D seismic projects from six to eight months down to one to two months. Great tools to have for Fast Track projects. These Smart Migrations algorithms use the information inside the prestack data for migration as opposed to standard depth migration algorithms like Kirchhoff migration or reverse time migration (RTM), which assume every image point in the subsurface is a diffractor and make no a priori assumptions about the migrated image structure. Instead, Smart Migrations analyze the input data, build a database of locally coherent events, and use these events to guide the direction of the migration operator. This knowledge of the input data can speed up the processing time by 100 to 500 times. Kirchhoff and RTM are Brute Force Migrations, at the opposite end of the computation power spectrum from Smart Migrations.

The Smart Migrations class of algorithms is a perfect response to the industry need to handle large amounts of data in short time. One early and successful commercial implementation of a Smart Migration class algorithm is the Fast Beam Migration (FBM) developed by John Sherwood at Applied Geophysical Services. The speed of FBM is achieved in two steps:
1. A factor of 10 in speedup is achieved using beam forming, or beam decomposition of the input data, where the number of input data is reduced by a factor of 10.
2. A factor of 10 to 100 in speedup is obtained by spreading each input trace or beam over a beam instead of a full aperture-volume.

The first step of the FBM decomposes the seismic data in wavelets, 100 to 200 ms in time duration. The beam formation reduces the input data volume by a factor of 10 and is an important factor in the speedup achieved with FBM. For larger reduction factors, data is not invertible; the original data cannot be fully recovered from the data beam. But at a larger data compression rate, the beams increase the signal-to-noise ratio and retain the coherent energy in the data. The migrated image appears almost free of artifacts due to this signal-to-noise enhancement and makes the image easier to interpret in complex areas.