25-Nov-06 10:00 AM  CST  

New Ideas and Their Diffusion 

Arthur E. Berman

Twenty years ago a friend and I had a brilliant idea:   if people could pay for their groceries with a credit card instead of writing a check, supermarket lines would move more quickly and the companies that owned the stores would have immediate payment.  We researched the concept and consulted experts who told us that the idea would never work for a variety of reasons that aren’t important now.  What is important is that many of us now routinely pay for groceries and most other purchases with a credit or debit card.  I don’t even have a check book anymore and only use a few computer-generated checks each month to deal with the rare and somewhat backward companies that don’t yet have a way to pay bills electronically.

Not everyone, of course, has embraced electronic payment of purchases and bills.  I sometimes still find myself behind someone in line at a store who is writing a check, showing their driver’s license and, ironically, waiting for electronic approval of their check.  While it seems inevitable that some day everyone will abandon check books, it may actually take years or a generation before the newer idea of electronic payment has become the norm.

This raises two questions that are pertinent to us as geologists and scientists:  how do new ideas originate and how do they spread?  I believe these questions go to the core of geological inquiry and human psychology.  After a quick look at the history of new ideas that have been accepted in science, I’ll describe how this applies in the petroleum exploration and production business.

Consider the beginnings of geology.  The ideas of James Hutton and Charles Lyell about the Earth, its age and the processes that governed the development of the crust and its sedimentary strata were considered radical in their time.  These ideas were not easily or quickly accepted by the contemporary scientific community, much less the educated public.  New notions about the Earth’s history along with ’s observations on the origins of life a few years later significantly disturbed the belief structure of 19th century society.

New Ideas:  Archetypal Ideas, Theories, Discoveries and Inventions

The origin of new ideas has been debated since at least the time of Socrates.  I will briefly and humbly add my views on where ideas come from to those of Plato, Descartes, Leibniz and Kant. 

It seems to me that there are relatively few truly new ideas.  Ideas of God, the spirit or soul, the after-life or reincarnation may be examples of truly original thought. The idea of tools, language and writing must have originated more-or-less independent of observation or explanation.  These are what may be called primordial or archetypal ideas and may, in fact, prove to be somehow hard-wired into our psyche, stored in our DNA, or to be self-replicating by some mimetic process.  

Many new ideas in science are really theories.  Theories attempt to explain phenomena or make mysteries somehow comprehensible, but are based on conjecture and probably lie beyond experimental or tangible proof.  Newton’s theory of gravitation, Darwin’s theory of evolution and Einstein’s theory of relativity, while based on observational science and at least partly supported by fact, represent astonishing and unifying insights that place them in the rarified realm of new ideas.  A structured theory may be called a model.

Most new ideas are really discoveries or inventions.  Discoveries and inventions result from observation and experimentation, respectively, and often arise from trying to solve a problem. Use of fire was probably discovered after lightning ignited a tree or a rock falling on another rock produced a spark: these were observations.  Learning to produce fire was an invention and was probably successful only after considerable trial-and-error.  The wheel likely was an invention that resulted from observing a rounded object—a tree limb or a rock--roll.  Similarly the development of agriculture or domestication of animals probably was more of a discovery based on an observation than a truly new idea.  We observed something growing that we could eat, discovered its seed, and experimented to grow the plant intentionally.

Most great scientific advances are likewise inventions or discoveries rather than new ideas.  Steam rising from boiling water developed into the hypothesis that perhaps a gas was a form of matter; experimentation with fire, water and machine produced the steam engine.  The train resulted from an experiment to marry the steam engine and the wheel with a modification—the track.  The automobile resulted similarly from the observation that ethanol (or, later, petroleum) produced energy and, combined with the wheel and an engine, could result in a new invention, the car.

Discovery may be spontaneous and un-premeditated depending on the circumstance or point of view.  The discovery of penicillin, for example, is commonly portrayed as a kind of accident whose significance was immediately grasped by Alexander Fleming. In fact, the development of penicillin occurred over a period of several decades, beginning with Louis Pasteur’s discovery of the antibiotic properties of certain bacteria. Fleming accidentally discovered the particular strain of penicillin but it was Howard Florey after him who finally perfected the culturing of the bacteria for medicinal application. 

The ability to identify and deduce new connections and patterns in nature and technology is the basis of experiment and invention. The development of new ideas is seldom an individual phenomenon but it born in the creativity of the collective ingenuity of the many.

The Origin of New Ideas

The impetus for new ideas originates in personal experience that is, above all, grounded in the moment, in the present and, for the most part, separate from the ego or the intellect.  New ideas arise when the individual is ripped out of the mundane perspective of self-conscious, material reality. In this state, he is somehow free to directly experience the relationship of his observations in a new way that is outside the influence of accepted thinking and judgment about how things are related or are supposed to be. 

We are all familiar with this state from dreams that we remember.  In dreams we experience the events and characters of our ordinary existence in a totally non-judgmental and un-intellectual way that surprises both our dreaming and subsequently awakened selves.  The mundane is transformed into the mysterious, and thoughts and experiences merge to form ideas that are sometimes so strange that it is hard to reconcile them with anything that comes from within ourselves.  In short, we are able to expand beyond what we and others have thought or believed, and open ourselves to new connections and associations. 

To be sure, this is also the realm of the mystic or shaman, but it is also where I believe all new ideas and insights come from in science.  This realm is embodied in all of our myths and symbolic stories.  The hero invariably goes into the underworld, the forest, the labyrinth or under water, where he experiences a strange and dramatic struggle or adventure.  He emerges back into ordinary reality transformed and proceeds to teach those left behind what he has learned.

Models and Problem-Solving

In its simplest form, a new idea is seeing a new pattern or relationship out of familiar observations.  Scientific knowledge is based on observations of nature. From observations, scientists try to identify patterns and create generalizations to explain underlying processes. A model is a simplified view or abstraction of reality that permits a restructuring of otherwise unrelated or mundane observations into a powerful and dynamic pattern.  Models commonly result in testable hypotheses and predictions. 

A new idea can be little more than recognizing an assumption and then trying a different one.  A good example may be found in Copernicus’ idea about the structure of our solar system.  For centuries science and religion had viewed the solar system, and indeed the universe, as having Earth as its center; Copernicus imagined what would change if a different assumption were made, namely that the sun was the center.  This, of course, changed everything. 

I went to a lecture several years ago in which the speaker, Deepak Chopra, a scientist, explained explains that most of modern science is based on the perfectly valid assumption that matter is primary and consciousness is secondary.  It is, he pointed out, an equally valid assumption that consciousness is primary and matter is secondary.  If we allow ourselves to consider that other possibility, imagine how our entire structuring of scientific reality must be re-organized!

In 1869, Dmitry Mendeleyev had despaired in his effort to discover a way to order the known elements in a meaningful and logical way.  He reportedly fell asleep and in a dream saw a vision or a model of how the elements should be ordered. He awoke and wrote it down. That became the periodic table of elements and it is essentially unchanged today except for the addition of a few newly discovered elements.  His accomplishment is even more remarkable because he devised the Table without knowledge of atomic structure.  More than 30 years would pass before J. J. Thomson (who discovered the electron) suggested that the electronic configuration of atoms might account for the periodicity of the elements, and more than 40 years would pass before atomic numbers were recognized as the basis for ordering the elements.

In geology, there are abundant examples of models that have resulted in profound new ways of thinking and structuring of observations.  In my lifetime, for example, both plate tectonics and sequence stratigraphy have emerged as vital new models.  Following the emergence of a new model, previously understood situations and observations are re-examined and often re-interpreted.  Geosynclines became fore- or back-arc basins.  Distributary channels become incised valleys.  At first these modified interpretations may seem trivial and are commonly dismissed by skeptics as nothing new, just a new set of jargon to confuse and confound what was already perfectly clear.  In time, however, familiar events and situations come to be understood differently and this results in new ideas about patterns, connectivity and opportunities.  In fact, models often cause us to look for a predicted outcome where none had been sought before.

The impetus for many new models and inventions comes from problem-solving and critical thinking.  When things don’t work well or correctly, scientists try to identify the cause of perceived malfunction or error.  Problems result when conventional or traditional ways of doing things no longer provide satisfactory results.  Often application of a different model reveals the cause of the problem and solutions can be provided and tested.  Has reality changed or just our perception and structuring of the observations that make up what we think of as reality? 

For centuries science had good and acceptable explanations for many observed phenomena.  Then introduced the model or theory of gravitation to reality and for centuries scientists went about re-examining and re-interpreting previously comfortable explanations in a new light.  Once the world was again comfortably understood, Einstein developed new models to explain the same phenomena and, once again, reality needed to be re-structured to fit the new model.  Reality, we find, does not change but the way the human mind structures reality does change and that makes all the difference.

Diffusion of New Ideas

Diffusion Theory deals with the manner and timing of the spread or diffusion of new ideas.    Diffusion Theory is a model and, as such, explains previous observations in a new light.  It happens to be the reigning model for the spread of new ideas but there are others and, in time, it will undoubtedly be replaced. 

The development of modern Diffusion Theory began in earnest in 1928 when a new hybrid seed corn was developed at The new corn was better than regular corn in every way.  It grew faster, produced larger corn, required less food and water and was more insect-resistant. Hybrid corn yielded about 20 percent more per acre than the open-pollinated varieties that it replaced.  It was also more drought-resistant and better suited to harvesting with mechanical corn-pickers. 

The sociology department at the University decided it would be interesting to track the adoption of the new hybrid seed corn by farmers.  The results were published in 1943 by Bryce Ryan and Neal Gross in Rural Sociology.  Ryan chose hybrid corn as the focus of investigation on social factors in economic decisions.  His objective was to study how an farmer’s social relationships with his neighbors influenced the individual’s decision to adopt hybrid corn.  Gross, a graduate student in sociology, was hired as a research assistant on the hybrid corn diffusion project.  Ryan and Gross selected two small communities located west of , and proceeded to interview all of the farmers living there. 

Over the course of the study period 1928-1941, all but two of the 259 farmers studied had adopted the new hybrid corn.  When plotted cumulatively on a year-by-year basis, the adoption rate formed an S-shape curve over time.  After the first five years, by 1933, only five percent of the farmers had adopted the new corn.  By 1936, 40 percent had decided to adopt the hybrid corn.  Then the rate of adoption leveled off as fewer and fewer farmers remained to adopt the new seed.

Farmers were assigned to categories based on when they adopted the new seed.  The five segments of farmers who adopted the hybrid corn seed, or adopter categories, and their percentages relative to the study group are:

(1) innovators (5%),

(2) early adopters (10%),

(3) early majority (35%),

(4) late majority (35%), and

(5) laggards (15%).

 

Compared to later adopters (Early Adopters Early Majority, Late Majority, Laggards) Innovators had larger-sized farms, higher incomes, and more years of formal education.  The innovators were judged to be more cosmopolitan, as measured by their number of trips to Des Moines (’s largest city, located about seventy-five miles away).  This first group of farmers, most importantly, had the ability to both understand and apply complex technical knowledge, and to cope with a high degree of uncertainty about new ideas or technology. 

In other words, the innovator group was capable of making a decision based solely on information. This group also had the financial means to be able to take a risk.  In this respect, the categories of adopter groups in the Diffusion Modeel can be correlated to their financial means and tolerance to risk.

The second group,  the Early Adopters, were typically respected members of the rural community and often were in dual roles as both farmers and role models in the banking, real estate, government, educational or religious institutions of the area.  This group was highly successful and had the highest degree of opinion leadership and peer respect among all the categories in the Ryan and Gross study.

The third group, the Early Majority, was characterized by frequent social interaction with their peers but seldom had positions of opinion leadership.  This group tended to undergo considerable deliberation in every decision. 

The Late Majority group represented fully one-third of the total population studied and, while generally skeptical and cautious, was most susceptible to the influence of peer pressure. This group was often guided by economic necessity since its members were among the less financially successful in the community.

The final group, the Laggards, generally had no opinion leadership in the community, tended to be somewhat socially isolated, was suspicious of new ideas and had limited financial resources.  This group is characterized by the over-my-dead-body philosophy of change.

The typical farmer moved slowly from awareness and knowledge of the innovation to adoption despite the obvious, objective advantage of the new corn over the open-pollinated variety it was adopted to replace.  The innovation-decision period from first knowledge to the adoption decision averaged about nine years for all respondents in spite of the tremendously successful results of farmers who first adopted the new seed.  In addition, the average respondent took three or four years after planting his first hybrid seed, usually on a small trial plot, before deciding to plant 100 percent of his corn acreage in hybrid varieties. 

The critical insight in Ryan and Gross’s study is that only the first group, the Innovators, based their decision to adopt the new corn on information.  The middle groups of adopters decided to try the new technology based on the opinion or experience of others.  The latest groups to adopt the hybrid corn seed were motivated more by momentum than information or opinion.

Applications of Diffusion Theory in Petroleum Exploration

Another way to look at Diffusion Theory distributions is to consider the percent of opportunity that remains for each successive adopter group.  This approach is important, for example, in petroleum exploration where a finite, economically attractive resource is available in a given play or basin.  Figure 2 shows the same percentage information presented in Figure with remaining opportunity shown as the difference between the cumulative percentage and the total.

 

The last member of the Innovator group to enter a particular play or basin still has 95% of opportunity available whereas the last member of the Early Majority only has 50% available.  One might conclude that, considering the risk avoided by an Early Majority play entrant, 50% of remaining resources is not bad.

Figure 3 shows proved reserves for the three main plays in the An Early Majority entrant into the Miocene play, for instance, in the U.S. Gulf of Mexico should have half of 18 billion barrels of oil equivalent (BBOE) proved reserves, or 9 BBOE, available to him.  The problem with this logic is that generally the largest fields are discovered early in the exploration history of a play.

 

Figure 4 tells a very different story.  In the first half of the Miocene play, between 1945 and 1971, about 10.5 BBOE of proved reserves were discovered versus only about 7.5 BBOE during the period 1972-2000.  If we look more closely we find that the average field size from 1945 to 1971 was 75.5 MMBOE whereas the average field size for the period 1972-2000 was only 18.9 MMBOE.  To compound the problem, the average field size in 1949, the third year in which discoveries were actually made (no discoveries were reported until 1947), the average field discovered was 138 MMBOE while in 1972 the average field discovered contained only 18 MMBOE proved reserves.

 

While discovery rates for the Miocene play in the Gulf of Mexico are somewhat typical of basins and plays around the world, it is worth noting that the Gulf of Mexico is unique as a long-lived prolific basin and has had a large number (at least 25) of significant discoveries (>50 MMBOE) in every year since the 1920s (Nehring, 1991).  In other words, in most other basins in the world, a much smaller field size would have resulted for the same Early Majority play entrant relative to the field size available to an early entrant.  On the other hand, at least in the , other plays, notably the Pleistocene play, saw the largest field size and greatest proved reserves discovered in 1971 over the same 55 year period.

The point of the analysis is that in petroleum exploration, economic success is generally dependent on being in the Innovator or Early Adopter group of basin or play entrant groups.  Those players who enter later have a large total percent of opportunity and number of barrels of oil equivalent available to them, but in much smaller field sizes and, therefore, in less economically attractive projects than the first entrants. 

This, I believe, explains why, for instance, in the Deepwater Gulf of Mexico play, the early players who stayed in the play—Shell, BP, and BHP among others—dominate the play with the largest fields and the most favorable economics.  Companies that entered the play in the mid-1990’s were already relegated to higher risk, ultra-deep water or sub-salt opportunities, and at considerably more competitive bids than the minimum bids required for the early players to capture leases and opportunities. 

The companies that have entered the Deepwater play in recent years must have very different overhead structures, costs of capital or strategies in order to make money.  If a player wants to enter somewhat late in the exploration history of a basin or a play, he must be willing to accept smaller reserve additions with their correlative economic implications; or he must enter with a new technology to reduce risk and/or cost, a different entry strategy (e.g., acquisition) or, better yet, a new play type that effectively re-starts the clock and allows him to enter in the Innovator or Early Adapter group. 

It is worth re-visiting the data from Figure 4 to examine the affect of new technology since this theme has been dominant in rejuvenating many plays and basins in recent years.

In figure 5, immediately following the year of our hypothetical Early Majority entrant, we see a “technology bounce” that persisted until 1991, most probably related to “bright spot” advances in seismic technology.  It is also likely that this “bounce” was related to opening of new areas for leasing, advances in drilling technology and associated, improved economics.  During that period nearly 334 MMBOE of new reserves were found each year, compared to a finding rate of only 119 MMBOE in the three years before (1970-72) and 93 MMBOE in the three years following (1992-94).  These are the sort of results from technology that get companies very excited about renewed opportunity for economic benefit, particularly those companies in the Innovator and Early Adopter groups relative to this new wave of technological advances.  In other words, the Diffusion Theory clock is reset for the exploration/production play when a significant new idea or technology comes along.

 

Unfortunately, a lot of companies apparently had the same idea during this period of technology bounce and, despite the impressive increase in overall proved reserves discovered, the average field size of almost 21 MMBOE was not appreciably larger than the average field size of about 19 MMBOE discovered for the entire period 1972-2000 (Table 1).  Given increases in technology costs and lease bonuses that resulted from the competition and enthusiasm, it is doubtful that companies made any more profit from their efforts during the technology bounce than they would have without it!

It is worth noting that oil field service companies are primary change agents in the spread of new technology in the E&P area, playing a similar role as the of researchers and salesmen who first promoted the new hybrid seed corn in the late 1920’s.  It is possible that a few Innovator companies, possibly working in concert with service companies, actually lead the new wave of technology.  Additional service companies and E&P companies quickly follow in the Early Adopter and later adopter groups.

Lessons Learned and Hope For

In petroleum exploration, as in much of life, timing is everything.  Since successful oil and gas exploration depends on new ideas, technology, economic analysis or concepts, it is important to have a way to assess the risks and rewards that accompany a decision to initiate or enter a play or basin.  Diffusion theory provides a relatively simple and generally applicable method to understand and calibrate exploration decisions. 

There has been much attention and emphasis in recent years on risk analysis as a means to both calibrate and rank E&P portfolio opportunities and to minimize exposure to low success probability projects.  It is instructive to consider the way oil companies have evolved in terms of the Diffusion model.  Figure 6 shows how the categories of innovator and adopter groups in Diffusion Theory may be used to track the maturity stages of an E&P company, an exploration play or even an industry.

In the early stage of a company’s history, it is dominated by technology enthusiasts. In the early days of the E&P industry, at the beginning of the 20th century, oil exploration was dominated by individuals and small companies who used cable tool technology to explore for and discover petroleum.  There was not a lot of science involved and the focus was on drilling and risk-taking.

Almost as soon as the viability of petroleum as a major energy source was shown with the discovery of Spindletop in 1901, visionaries like Rockefeller stepped in to imagine and form corporations that could apply drilling and subsequent technologies to a larger scale of exploration and production.  Companies like Standard Oil and Texaco were formed and flourished.

As the industry progressed, pragmatists entered the picture who believed that new technologies could be developed to reduce risk and cost. Research centers were formed by the oil corporations that developed improved technology such as rotary drilling.  Service companies evolved that invented logging and geophysical technologies to reduce risk.  This pragmatic stage also saw the diversification of oil companies into full spectrum E&P organizations with up- and downstream exploration, production and marketing divisions. 

The late 20th century saw conservatives begin to dominate the oil industry. The primary focus shifted from oil and gas exploration and production to shareholder value and its emphasis on quarterly satisfaction of security analysts and investors.  Oil companies began to rely increasingly on acquisitions and mergers to create additional value.  Research laboratories were closed and new technology became the realm of service companies.  Outsourcing of other, previously core functions occurred along with research and development.

Risk analysis is an important component of the conservative phase of oil company evolution.  Risk analysis and portfolio management presume that the petroleum can be viewed as to commodity and that, with modern technology, much of the finding cost and risk can either be removed or, at least, effectively understood and managed.  The risk process is characteristic of an industry that drills very little without 3D seismic, some kind of direct hydrocarbon indicator, and a probability of success greater than 50%.  There are many plays where operators boast an 80% average probability of success rate.

I have never seen Diffusion Theory used as factor in risk analysis or portfolio management.  In other words, all other technical risks being equal, Where are we in the diffusion and adoption cycle and does sufficient opportunity remain to justify investment in a particular play or basin? 

I do not believe that the oil industry in the has yet entered the final stage predicted by Diffusion Theory, namely the Laggard or Skeptic phase but I will speculate briefly on what that kind of company might look like.  It would be largely a holding company that functioned principally as a bank to invest in specific plays and prospects that met certain extremely rigid, low risk criteria.  It would have almost no employees.  All science and engineering work would be done outside the company and would be presented in final, developed form by outside people who wanted funding for their project.  These projects may have been screened by representatives of the company prior to presentation to ensure they met the standards of quality and risk that were required, though this screening would likely be done by contractors.  Successful projects would be sold or otherwise monetized as quickly as appropriate returns could be achieved so the company would have almost no assets other than cash at any moment in time.

The Future of New Ideas

We live in a time where many of the world’s great conventional petroleum provinces are in or near decline.  Many major oil companies have disappeared in mergers and takeovers.  The most attractive recent plays are all in deep water simply because the considerable costs and risks have prevented exploration and development in these areas until recent advances in technology, and increased product pridce make this kind of play feasible.  Each of these new frontier provinces—deep water Gulf of Mexico, West Africa, Brazil, and India to name a few—can be readily analyzed in terms of Diffusion Theory.  Innovator, Early Adopter, Early and Late Majority and Laggard companies can be identified along with the correlative opportunity that remains to them based on the timing of their entry into the play.

We are in an age of profound conservatism among E&P companies in terms of the Diffusion Theory life cycle model I have described, and one of dwindling areas with attractive new, conventional resources of petroleum, In an era where few major oil companies still maintain research and development organizations, Where will the new ideas come from that will carry the oil industry to the next phase of exploration and development?  What incentive will there be for discovering new frontier exploration plays in an E&P culture that subjects all investment to stringent risk and portfolio analysis?  If the Diffusion Model were included in current risk analysis, I suspect that most would conclude that it is too late for new players to enter existing plays and only appropriate for existing players to develop what has already been discovered or identified.

It is time for oil companies to consider re-inventing themselves.  Diffusion Theory suggests that the current life cycle of both company evolution and exploration plays has reached an advanced stage of stagnation and morbidity.  E&P companies, if they are to survive, must re-establish new ideas and the inventive people who conceive them as the core capability of their organizations.  They must learn once again to take risk. The model of strategically managed oil companies has failed to find significant new reserves and it should be abandoned.  Senior management must re-involve themselves in the world of technical ideas.  This means they must once again evaluate and make decisions on new plays and prospects, and abandon the absurd notion that they are business people who can afford to leave science to technical risk committees. 

I do not know the solution to diminishing new reserves of conventional petroleum, but without a deliberate company culture that fosters, values and rewards new scientific ideas and new thinking there will be no relief.  It may be that the future of exploration lies in unconventional petroleum reserves—tar sands and gas hydrates.  Whatever the answer, it will only be found with new ideas, new theories, new inventions and new discoveries.  It has always been this way throughout the history of our species. 

 

**The history behind the theory of diffusion of innovations can be traced back to the beginning of the 20th century in German–Austrian and British schools of anthropology as well as French sociologist, Gabriel Tarde.

 

Bibliography

Dawkins, R., 1976, The Selfish Gene: 

Gladwell,  M., 2000, The Coolhunters, The New Yorker (

Nehring, R., 1991, Oil and gas resources, in; , A., ed., The Gulf of Mexico Basin: Geological Society of America, Decade of North American Geology, v. J, p. 445-494.

Norman, D. A., 1998, The invisible computer: Why Good Products Can Fail, the Personal Computer Is So Complex, and Information Appliances Are the Solution. ,

Rogers, M., 1962, Diffusion of Innovations: The Free Press, New York.

Ryan, B. and Gross, N. 1943, The diffusion of hybrid seed corn in two communities: Rural Sociology, 8 (1), p. 15-24.

 

Thanks to Josh Rosenfeld, Tom Feldkamp and Frank Walles for their comments and suggestions.

 

 

 

 

 

 

 

 

 

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Arthur Berman
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Source: Arthur E. Berman
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