“California has the potential to be the leading supplier of lithium in the world,” said California Energy Commission Chair David Hochschild.

It’s a bold statement from the state level, but one that is backed by both a growing demand for the metal and the development of new geothermal extraction technologies.

Lithium is a key ingredient in lithium-ion batteries that are not only used in many everyday personal tech items like laptops and cell phones, but are crucial to the operation of electric cars and the storage of energy that helps burgeoning renewable industries that cannot generate power 24/7. For example, new solar farms in California are being built with lithium- ion batteries, which can provide a few hours of electricity after sundown. And many major original equipment manufacturers (OEMs) in the auto sector and EV battery manufacturers have been seeking a secure U.S. lithium supply. Yet currently, it is China, Japan, and South Korea that produce 85 percent of the lithium chemicals required to power electric vehicles.

“We’re on the brink of a boom in the geothermal industry,” explained Will Pettitt, PhD, during his talk at the USEA 16th Annual State of the Energy Industry Forum in Washington, DC in January of this year. “It also means there are significant opportunities for collaboration across industries, and geothermal is at the center of that. Combine this win-win situation for the renewable energy sector with the benefits that geothermal can bring to the oil and gas industry, and development of critical mineral recovery, then it means that geothermal can facilitate collaboration across wide and disparate parts of our energy and mineral sectors for the benefit of everybody.”

Geothermal power can provide both the baseload 24/7 power and the brine that is rich in lithium. Though extraction is not new to the geothermal industry, it has had its challenges in the past. “One of the issues with previous attempts was ‘bolting-on’ lithium extraction systems downstream from existing power plants. These systems were basically ‘force-fed’ spent brine which was not ideal, “explained Controlled Thermal Resources (CTR) Chief Operating Officer Jim Turner, who has managed geothermal plants in the Salton Sea region for over 20 years.

On March 16, CTR formally announced its technical partnership with direct lithium ex- traction specialists Lilac Solutions, which has recently developed a new extraction technology using unique ion exchange beads developed by its team in Oakland. This is a significant deviation from prior efforts within the industry, where the focus was to adapt conventional aluminum- based absorbents that did not perform well.

“Lilac Solutions’ technology offers exceptionally high lithium recovery and lithium selectivity, enabling low cost production,” said Turner. “The Lilac team has proven these results with thousands of hours of test work on the Salton Sea geothermal brine chemistry.”

Lilac also confirms that it has conducted numerous large-scale tests on brine samples from around the world and received independent verification on the performance of the technology, which is significantly cleaner, faster, cheaper, and more scalable than existing mining technology. CTR hopes to create a major new domestic source of this mineral and is currently negotiating additional contracts for power and lithium sales.

In fact, the amount of interest that has been generated within the state around lithium extraction was reflected in the proposed awards announced on March 20 by The California Energy Commission (CEC).

CTR was awarded net funds of $4,460,334 for two projects related to its Hell’s Kitchen Geothermal LLC Lithium & Power Project: improved silica removal for enhanced geothermal plant performance and its Geothermal Lithium Extraction Pilot.

BHE Renewables, part of Berkshire Hathaway Energy, also won a $6 million grant funding opportunity award to build a lithium recovery demonstration project at their Salton Sea Geothermal Lithium Recovery Demonstration Project near Calipatria.

In addition, Materials Research LLC, based In Palo Alto, had a proposed award for pilot scale recovery of lithium from geothermal brines for $1,878,634.

These awards signal an endorsement of the emerging lithium industry by California’s government, and a commitment to supporting emerging geothermal-related solutions that could power the world’s technologies.

As Pettitt reflected in his talk at the USEA Forum, “We are now inside an energy revolution. Society will need a mix of renewable and clean energies as well as a huge leap in energy efficiency and heat management. An “all-of the-above” clean energy strategy. It’s clear that society needs geothermal now.”

Did the Vikings ever imagine so much heat lay beneath the snow and ice around them—although the climate was a bit warmer back then? Probably not, but why should they? Most of them hailed from Norway, a country with geological stability; a land of ancient metamorphic and sedimentary rocks; and one with no signs of heat but for a tiny spot in the far north and another on the ocean floor. Sure, earthquakes rumbled through occasionally and a few warm springs bubbled in that Arctic archipelago, but the pools of water in the quiet Norwegian woods were cold, and the forested mountains didn’t spew vast clouds of steam, ash, and rock high into the sky; turn snow and glaciers into roaring rivers; or extrude flowing, fiery ropes and sheets of immolating lava. The Norwegian mountains were tree-covered and still; they weren’t volcanoes.

Beginning around 863 A.D., or a bit earlier, Viking ships, many from Norway, began making short Icelandic stopovers. The trip from Norway took about four days in good weather.

In about 871 A.D., a large volcanic eruption dusted most of Iceland in a thin layer of volcanic debris, today called the Landnám tephra. (Tephra is a mixture of fragmented, volcanic products, like ash and cinder.) The tephra layer helps to date Icelandic settlements around the island, for no indications of Viking life have ever been found beneath this layer—except for one grain of barley pollen near the capital city, Reyjavík.(2)

The Icelandic Age of Settlement dates from 874 to 930 A.D., a time of great change. In 874 the first group of Viking farmers moved permanently to Iceland. They came from Norway, and their leader, Ingólfur Arnarson and his wife Hallveig Fróðadóttir, brought along family members, farmhands, and seasick sheep and cattle. On reaching the shore, they climbed freezing from the cargo ship, called a knarr, to begin their new lives.

As they stepped on Icelandic soil, they were standing not only on an island with nine active volcanoes, but one with five of the volcanoes close by, arrayed along the southern coastline. From 874 to 930, the Vikings would build many settlements among the five volcanoes—including Ingólfur and his family, who settled down in a place he called Reykjavík.

Ingólfur chose Reykjavík for his home in an unusual way. Before landing in Iceland, but with land firmly in sight, he tossed his pair of carved, sacred, high-seat pillars overboard—vowing to settle wherever they washed ashore. According to legend, after three years of searching he found the pillars at the edge of a bay he named Reykjavík (“smoky bay”, from the Old Norse) for the active hot springs and fumaroles around it.(4) Eventually many other Icelandic sites were given geothermal names, as well.

It turns out the Vikings weren’t the first to settle in Iceland. Christianity arrived before them, brought to the island by hermitic Irish monks who lived in caves—possibly inside the lava tubes. Commenting on the long Icelandic days and short midsummer nights, a monk once wrote, “... whatever task a man wishes to perform, even to picking the lice from his shirt, he can manage as precisely as in broad daylight.”(4) When the Vikings arrived, the monks fled in a hurry, leaving behind many personal effects in the rush to get away.

Mount Hekla, one of the five southernmost coastline volcanoes—and still very active, has erupted well over 20 times since 874 A.D. During the fiery eruption of 1104, huge blankets of tephra destroyed at least 20 farms in southern Iceland. One such farm, named Stöng, has been excavated, rebuilt, and opened to the public. The farmstead is preserved perfectly, like an Icelandic Pompeii. The sod walls were left nearly intact beneath the thick layers of tephra.(4)

Inspired by the 1104 eruption, monks began spreading tales throughout Europe that Mount Hekla was a gateway to Hell. This speculation is illustrated dramatically in books and maps from The Middle Ages—as you can see on the 1585 map of Iceland reprinted above. Notice how Mount Hekla, the largest feature on the map and one covered in flame, spews dark smoke and volcanic bombs—the arc of black dots above the smoke.

Dr. Sindrason worked for the Nordic Volcanological Institute from 1975 to 1984. During that time, he and others studied Krafla, a volcano mentioned in the hot springs photo caption.

When I asked Dr. Sindrason about his Viking ancestry, he said it was “a bit too long ago.” He thought I had meant family stories from the last several hundred years, but I’d really wanted him to go back 1,000 years or more. Impossible because he was right; the events were “a bit too long ago.”

Back in 930, 201 years before the 1104 eruption of Mount Hekla, the first Icelandic national assembly, called the Althing, met at Thingvellir (“Parliament Plains”) about 50 km from Reykjavík. Today Thingvellir is a National Park and an UNESCO World Heritage Site. Its long and incredible expanses of lava form part of the mid-Atlantic ridge.

The Vikings created the Althing to establish a common code of law suitable for a republic (not a monarchy) and to settle disputes. Here was where the Law-Speaker spoke to the assembled Vikings in front of the great wall of lava. “The riven wall of lava made a splendid sounding-board for speakers’ voices in those days before microphones and amplifiers were invented.”(4)

By the year 1000, just 70 years later, the King of Norway was pressuring Iceland to renounce paganism and accept Christianity. Icelandic political opinion on the matter was polarized into two bitterly opposed factions. War seemed imminent. Sensing a crossroad, the leader of the Christian party, Hall of Siða, asked the Law- Speaker of the Althing, a pagan named Thorgeir Thorkelsson, to arbitrate. Everyone swore to follow his decision.

After considering the situation for a day and night, and in the interests of peace, Law-Speaker Thorkelsson declared that all unbaptized people in Iceland should become Christians and be baptized—meaning they would be submerged in cold water. Now real trouble arose. The Icelanders agreed to become Christians only if hot water was used in the ceremony.

And so it was. People from northern and southern Iceland were baptized in a hot spring named Reykjalaug (later called Vígðalaug, “the consecrated spring”) at Laugarvatn. Those from western Iceland were baptized in a hot spring named Lundarreykjadalur (later called Krosslaug, “the spring of the cross”) at Reykjalaug. Since then, both of these hot springs are said to hold healing powers.(3)

Iceland lost its independence to the King of Norway in 1262 and together with Norway to the Queen of Denmark in 1388. Toward the end of the thirteenth century, the Norwegian Archbishop of what today is Trondheim apparently won exclusive rights to buy and transport native sulfur from Iceland. The sulfur was collected from locations known today to include high-temperature geothermal fields. Exactly what the sulfur was used for is unclear, as gun powder hadn’t yet been invented.(3) Some say the Catholic Church may have exported the sulfur to European churches so the congregations could grow familiar with the odor of hell.1 Icelandic sulfur rights—sometimes quite valuable—were controlled by the Danish King for many years until the rights were returned to Iceland in the 1760s.(3)

How many hot springs are in Iceland? The count includes 250 thermal areas and 600 major hot springs. Surprisingly, geothermal expert Ingvar Fridleifsson finds no correlation between natural hot spring locations and the sites where early Vikings chose to build the farmhouses. Even the first settler, Ingólfur Arnarson, built his own farmhouses about 3.5 km away from the nearest hot spring, apparently preferring a good landing beach for his boats to the luxury of nearby hot waters for things like bathing, laundry, and cooking—not his duties, after all.(3) Several years ago, parts of his farmhouse were discovered under the southern end of Reykjavík’s Aðalstraeti (“Main Street”) that runs down to the harbor.(4)

In the eighteenth century, hot springs were considered “nuisances” by some farmers who were quoted in a book titled, Description of the Farmsteads of Iceland, 1703-1714. One farmer said, “A part of the hay field is spoiled by a hot secretion caused by a nearby hot spring.” Another complained, “Storms are fierce so that both houses and haystacks are in danger. The water is warm (read ‘undrinkable’).”(3)

Only one pre-twentieth century archaeological structure exists where hot spring waters may have been used for space-heating. This is in the farmhouse (barely seen in the photo) built behind a hot water pool called, Snorralaug (“Snorri’s bath”), in the village of Reykholt. Snorralaug itself is the only ancient (probably 13th century), man-made bathing structure still standing in Iceland, and it resembles a 4 meter wide, stone-sided, backyard hot tub.

Built by powerful chieftain, famed historian, and writer of sagas, Snorri Sturluson (1178-1241), Snorralaug was heated with steam and hot waters flowing through two conduits from a nearby hot spring, called Skrifla. A third conduit from the hot spring bypassed Snorralaug entirely and went straight to the farmhouse in back. Were at least some farmhouse rooms warmed by geothermal heat? Perhaps, yes.

We do know that for over a millennium, thousands of Icelanders living in a very cold climate never used the nearby hot waters to warm their houses. In 1908, perhaps the first person to do so was a farmer at Reykir in Mosfellssveit, Stefán B. Jónsson (1861-1928). He brought hot spring waters through a 2.3 km pipeline into his home to heat the radiators. Not long after, in 1911, Erlendur Gunnarsson, from Sturlureykir in Western Iceland near Reykholt, invented a simple mechanism to separate the steam from the hot water in a boiling spring next to his house. He used the steam for cooking and heating.(3) The two installations, invented over a century ago, began the modern era of geothermal development in Iceland.

1. Björnsson, L., 1994, Personal communication, from Fridleifsson, Ingvar Birgir, 1999, Historical Aspects of Geothermal Utilization in Iceland, in “Stories from a Heated Earth,” Cataldi, Raffaele, Hodgson, Susan F., and Lund, John W., eds., published by the Geothermal Resources Council and the International Geothermal Association, Davis, California.
2. Fitzhugh, William W. and Ward, Elizabeth I., 2000, Vikings, the North Atlantic Saga, published by the Smithsonian Institution Press, in association with the National Museum of Natural History, Washington, D.C.
3. Fridleifsson, Ingvar Birgir, 1999, Historical Aspects of Geothermal Utilization in Iceland, in “Stories from a Heated Earth,” Cataldi, Raffaele, Hodgson, Susan F., and Lund John W., eds., supra.
4. Magnusson, Magnus, 1980, VIKINGS!, published by E.P. Dutton, New York.

Thanks to Brian Billings for helping with the Latin translation, and to Dr. Sigurjón Sindrason for kindly answering my questions.

Plaine de Garonne Energies is a joint venture between Storengy and Engie Cofely in charge of the construction of a large district heating network, mainly supplied by renewable energies, in Bordeaux (France). Geothermal is a major solution planned to provide the new Plaine de Garonne district with heat, on the right bank of the Garonne river.

In 2015, the Bordeaux Metropole called for a geothermal project, for supplying heat to several districts, with the additional ambition to explore deeper formations for even hotter resource, namely the Jurassic expected at a depth of ~1,700m.

Storengy proposed its expertise in geosciences and its experience in both exploration and drilling, as well as geothermal project development in Partnership with Engie Cofely.

Through the subsurface studies, Storengy geologists, reservoir engineers and well engineers were able to propose an innovative design of a doublet (of wells), in allowing for both exploring and yet to be characterized deep Jurassic layers, and ensure a fallback to the proven upper lying Cretaceous aquifer. This project is a challenge both technically and in terms of management because of exploration risks, budget constraints, and the upper stringent objective of delivering heat to the district network. To comply with all these objectives, vertical wells were designed with an innovative technical solution with no sidetracking.

This project illustrates that Storengy and Engie companies are fully committed in finding innovative and efficient solutions for deep and shallow geothermal energy supply energy to cities and communities.

By choosing geothermal energy as a low- carbon energy source for the heating network that will supply the new neighborhoods being built on the right bank of the Garonne, Bordeaux, Metropole has demonstrated a strong commitment to a greener future. The city’s elected officials are looking to deep-reservoir exploration to meet their energy needs. The project will build the necessary equipment to provide the public service of generating, transmitting and distributing energy for heating and hot water in the buildings in the areas covered by the contract, namely the communities on the right bank of the Garonne and more specifically the Brazza, Bastide Niel, Garonne Eiffel and La Benauge urban projects (Figure 1).

It will supply energy to the equivalent of 28,000 homes.

The contract for the future geothermal heating network was awarded to a consortium formed by Storengy and ENGIE Cofely, that joined forces for the project. They will now study, design, build and operate the facilities over a 30-year period under a public service delegation contract. The project is known as Plaine de Garonne Energies (PGE).

PGE’s shareholders are Storengy, which will contribute with its subsurface and drilling expertise and carry out the geothermal exploration and development, and ENGIE Cofely, which will build the energy production facility and the district heating network.

In 2018, the construction work on the main heating plant has been initiated and the first few kilometers of the network built. The remaining network construction activities will be carried out as the new residential areas are built. The wells will be drilled in the second half of 2019.

The geothermal system is set to be commissioned during 2020.

The project is focused on the use of geothermal energy, specifically the resource that is assumed to be present in the Jurassic layer some 1,700 m below the surface. At that depth, the water temperature is expected around 70°C.

The geothermal resource will be confirmed with tests to assess the water flow rate that could be achieved by the production well. Since there are no similar projects in the Bordeaux area, in-situ exploration is the only way to assess actual flow rates, examine re-injection options and determine the physical and chemical properties of the water. The first drilling operation could have two possible outcomes (Figure 2):

•    Total or partial success at the Jurassic layer: a well doublet that extends into the Jurassic layer will be implemented. A well, for re-injecting water, will be drilled into the same Jurassic aquifer. Heat pumps will increase the water’s temperature (70°C) and enable the full potential of the resource to be exploited.

•    Failure at the Jurassic layer: a fall back solution allowing for the exploitation of a proven reservoir in the Cretaceous layer, which is located 900 m below the surface and has a water temperature of 45°C, will be implemented. A number of wells in the area already use this resource. In this scenario, the well doublet will be performed at the Cretaceous level.

If the Cretaceous aquifer is used, the production of geothermal energy will be lower. A biomass heating plant will be added to the facilities to ensure that enough low-carbon energy is generated to meet renewable energy requirement (Figure 3).

In both scenarios, the future PGE heating network will be supplied with heat from the geothermal well doublet. Gas boilers will act as a backup and cover any consumption peaks (in the event of a cold snap), the aim being to supply 82% carbon-free energy.

PGE is a flagship project for several reasons. It is the first deep geothermal district heating project conducted outside Paris the area in 30 years (the most dense geothermal district heating area). It illustrates the expertise of Storengy and ENGIE Cofely in this market to satisfy the communities’ needs and the customers’ requirements.

At the beginning of the subsurface studies, we gathered the most complete documentation on 1/ already drilled wells from shallow to deeper ones reaching the bottom horizon for the Jurassic layers), 2/ previously published reports and logs performed describing geology, petrophysical properties and well tests information 3/ published interpretations of the regional and local geology and 4/ exploitation data of the wells such as flow rates and withdrawals over time.

All this information was taken into account in the assessment of the resources and the general sketch of the geothermal doublet (well location etc.), the well design, and to investigate the durability of the doublet.

The location of PGE1, the vertical producer well expected to be drilled first in the PGE project, was dictated by the location of the future heat plant. At the beginning of the project, no location was assigned to the injector well (PGE2). The identification of the location of PGE2 was part of the project, depending on the subsurface study and on the available yards on the right bank of the Garonne river; almost the full area being in renovation.

Regionally, around Bordeaux, 9 deep wells were previously drilled. 4 of them reached formations below the Jurassic layers of interest (Figure 4b).

Nearby the PGE1 location:

•    Bouliac is the closest well from PGE project that reached the Jurassic formation. It is located, in the southeast, about 6 km away (Figure 4c). It was performed for oil exploration. The well did not show much water, but has never been properly developed and tested for water production. Other Jurassic wells showed water. No information on water productivity was retrieved from logs and records. Few indications on petrophysical parameters are available and tend to show a heterogeneous formation.

•    Wells from Merignac to Lormont were drilled in the early 1980ies, for geothermal purpose. They exploited the Cretaceous Formation (Figure 4a and 4b). Petrophysical and water productivity information are quite well known.

The Cretaceous reservoir layers are constituted from top to bottom of approximately 180 m of dolomite, dolomitic limestone and recrystallized micritic limestone, and approximately 30m of alternating fine glauconitic sandstones and medium to coarse sands, which provides about 75% of the total flow rate (data from offset wells ).

The Jurassic reservoir layer is expected to be constituted of 2 sub-layers corresponding to, on one hand, gravelly bioclastic and oolitic limestone (with few sandy to microconglomeratic intercalations) and on the other hand, dolomitized micritic limestone. The porosity of these levels is expected to come mainly from fractures but also from clastic intercalations and dolomitized areas, with an unknown lateral extension.

The seismic data in the region were acquired in the 1970ies, avoiding the urbanized area of Bordeaux (Figure 5c). No new seismic campaign was performed since. From this information, and formation well tops, a structural model was proposed by the BRGM (2014 Figure 5a and 2008 Figure 5b) for the Cretaceous formation. No structural model was available for the Jurassic Formation, and we created our own structural 3 D model for the area (Figure 6c), including the Jurassic formation, despite a cruel lack of data for this level.

The 3D geological model (Figure 6c) includes the structural framework and the available petrophysical data.

The structural framework encompasses the fault network, the Upper Cretaceous top map and well data (Figure 6a). The underlying structure is inferred from this information and scare data from Jurassic wells; therefore, it presents consequent uncertainty.

The petrophysical model of cretaceous layer is fed by well logs inputs and well tests interpretations.

For the Cretaceous, both distributions of net-pay for the calcareous level and the sandy one are obtained from geostatistical simulations, conditioned to well data (Figure 6b). Those realizations allow to derive equivalent porosity and permeability properties.

For the Jurassic reservoir layer, scare data do not allow for an identical workflow. The porosity and permeability distributions of the Jurassic are considered constant (although suspected to be heterogeneous). Values are taken equal to the mean properties of the very well-known Paris Basin Dogger formation which appears to be the best analogue a priori (same depth, similar lithology, and also a proven geothermal resource). The two Jurassic reservoir sublayers are identified in the 3D geological model.

Given the uncertainties and the respective expected heterogeneities of the reservoir formations for both targeted formations, the 3D dynamical simulations where performed using the geological 3D model as an input. Net-pay realizations for both cretaceous intervals are conditioned to well data measurements. Equivalent permeability (and porosity) could be derived then (Figure 7).

For the Cretaceous Formation, as the aquifer is quite intensively exploited for almost 4 decades, the considered initial conditions correspond to the 1976 map of hydraulic heads. Historical withdrawals of the wells where introduced. The calibration of the permeability field was performed using two well tests performed in 1981: the long duration test of La Benauge (and associated interferences measured on Meriadeck and Lormont wells), and the long duration pumping test of Lormont.

The 3D fluid flow and thermal transfer model indicated the favorable locations for the injector well (as several options were initially available) allowing for a 30-year lifetime (without thermal breakthrough) for the geothermal doublet at the Cretaceous.

Maximal thermal and pressure impacts were assessed for both the Cretaceous (Figure 8) and Jurassic levels, after a 30-year period with the maximum flow rates (250 and 300 m3/h, respectively to the formations) and a 15°C reinjection temperature.

The impact of temperature at the producer well after 30 years is estimated inferior to 0.5°C, and the pressure impact is estimated to -0.6 bar at the closest well, La Benauge (not used since 2011).

Considering the complexity of the drilling project, and that reinjection is only possible in the exploited aquifer, all outcomes have been assessed (Figure 9), and the wells were designed to ensure all these outcomes.

The main principle for the design of the well was to drill in large diameters to allow for all possibilities in vertical wells, with no side tracking, while ensuring well performances to exploit the Jurassic or the Cretaceous.

At the first well (the producer), after reaching the proven layer of the Cretaceous, a short test will be performed to check the expected -proven- resource. The drilling will be then performed to the Jurassic layer and a mixed diameter liner covering entirely the well (except the Jurassic formation) will be installed. This liner will be then cemented but only on the interval separating the Jurassic from Cretaceous.

Both aquifers are then isolated, and the well is then producing only the Jurassic aquifer.

If the short and long duration tests of the Jurassic aquifer show performances suitable for geothermal exploitation, then the liner will be cemented through perforations, and the 2nd well will be performed at the Jurassic level too. If not, then the Jurassic at the first well will be closed/cemented, while the mixed liner will be opened at the Cretaceous the upper part of the liner will be cemented, and the Cretaceous completion (with gravel and screens) will be installed (Figure 10).

The design of the second well is somehow similar to the first one allowing for a possible fallback from Jurassic to Cretaceous. In the end, it is necessary to give the possibility for both wells to fall back in case of a Jurassic success at the first well but deceiving performances at the Jurassic at the second one.

The subsurface studies and the future drilling works (hopefully, successful wells) are part of large project meant to provide several districts with heat. Despite this paper focuses on the subsurface part, the project has also strong developments on surface installations and network. The joint value of Engie Cofely and Storengy partnership on this project resides on the handling of such a project, as well as the efficiency of interactions at the joint point constituted by interdependence of well heads and wells performances and surface installations.

This project illustrates the commitment of the companies on shallow and deep geothermal to serve the communities.

Storengy and Engie Cofely would like to thank the Plaine de Garonne Energies company and the Bordeaux Metropole community, and all their employees involved, now and from the beginning, in the project.

Bugarel, F., Desplan, A., Eveillard, P., Griere, O., Grange, M., Guttierez, T., Malcuit, E., and Piron, E., "Caractérisation des ressources géothermales profondes au droit de la Métropole Bordelaise - Conditions techniques et économiques d’accès à la ressource”, BRGM report, RP-64247-FR, Orléans, France (2014).

Platel, J.-P., Abou Akar, A., and Durst, P., “Etude sur les possibilités de valorization et de reinjection des eaux de rejet des forages géothermiques de Mériadeck et de La Benauge, commune de Bordeaux (Gironde)”, BRGM report, RP-56120-FR, Orléans, France (2008).

BEPH, web access for French geological data, http://www.minergies.fr/fr/cartographie,  2015.

At a moment when the tide of concern about climate change is reaching flood stage, it’s clear that the world is destined for a significant transformation in the production and consumption of energy. For example, in January, BlackRock CEO Larry Fink declared that “climate risk is investment risk,” and that “…there is no denying the direction we are heading. Every government, company, and shareholder must confront climate change.” Since BlackRock is the world’s largest investment firm, with more than $7 trillion USD under management, Fink’s proclamation that BlackRock will consider the environment as they assess investments may indeed be a turning point. Especially for the Energy industry.

Decarbonization efforts are gaining traction at the same time demand for electricity is increasing. Case in point: the increased commitment by automobile manufacturers to build electric vehicles. Volvo, Mercedes Benz, Porsche, Ford, and Nissan are among the companies that have announced their intentions to build more electric vehicles.

Recently, General Motors’ CEO, Mary Barra  underscored the trend: "GM believes in the science of global warming. We believe in an all-electric future. It’s not a question of if, but when." 

In the context of increasing reliance on electricity for buildings, transportation and manufacturing, and the search for renewable resources as a base load to complement intermittent sources and deliver the energy we need, Geothermal proponents have earned a seat at the power generation table.

Although popular perceptions might position us on opposing sides of the global climate change discussion, what can the Geothermal industry learn from our colleagues in the oil and gas industry?

Given the similarities in subsurface challenges and overlapping technologies, can their knowledge, deep pockets and vigorous efforts to transform business operations through “digitalization” yield valuable lessons for Geothermal operators?

One promising possibility is in the field of Artificial Intelligence (AI).

For the oil and gas industry, AI systems are useful at three stages of asset life.

• Early life – exploration phase
• Mid-life – planning and operations
• Mature life – data-driven expansion to obtain the most return from a reservoir or field

How can AI provide assistance to Geothermal operators?

A major pain point for Geothermal operators is cost of drilling to find reservoirs of naturally heated water, or subsurface heat sources into which water can be introduced to enhance reservoir performance. In oil and gas production, the financial risk is mitigated by the long-term value of the commodity produced. Oil is valuable, and a single successful asset can offset the cost of exploration and provide recurring value fordecades. In Geothermal, the value is inherently lower. Further complicating the problem is the fact that the drilling dynamics for Geothermal differ from oil and gas exploration. Oil and gas tend to be found in softer rock formations, whereas enhanced geothermal systems require drilling in harder igneous rock. Drilling in high temperature environments also increases costs and risks, especially in the exploratory phases. Geothermal resources such as Hydrothermal Systems, Enhanced Geothermal Systems, and Sedimentary Systems may all benefit from advanced Cognitive AI technology, especially at a time when Diverse Energy Resources (DER) are being integrated into grids by utilities and private enterprise. Predictive demand combined with flexible supply is a viable strategy for energy security, efficient production and distribution, and environmental benefits.

Beyond Limits, a California-based AI software engineering company, is commercializing cognitive computing and AI software technologies created by Caltech’s Jet Propulsion Laboratory for NASA missions over the last 20 years. There are many similarities between the problems of deep space missions tasked to explore the unknown realms of space and those faced by energy companies exploring the subsurface.

Cognitive computing is the most sophisticated form of artificial intelligence (AI). If you think of AI as a stack of capabilities, at the base layer of the pyramid is data analytics and at the pinnacle is cognitive computing - (see figure above).

Cognitive computing emulates human reasoning and uses knowledge provided by domain experts, not just data, to understand situations, recommend actions, and solve problems. Cognitive AI systems differ from conventional artificial intelligence applications as they can adapt and become smarter over time as they interact with more experts, problems and data. Cognitive AI systems are able to operate in complex situations where uncertainty exists and data may not be as prevalent as we would like, and in such situations, they analyze and solve problems much as a human would.

Beyond purely numeric AI approaches, Cognitive AI systems incorporate symbolic reasoning in the form of knowledge bases from human experts, enabling hypothetical reasoning scenarios based on best practices to arrive at recommendations for human operators. Predictions about the most promising locations to drill are detailed in transparent, evidence-based audit trails documenting confidence, uncertainty, and risk levels. This is known as explainable AI (X- AI) to differentiate from the black-box obscurity of conventional numeric machine learning AI approaches.

The technology is maturing rapidly, and in the energy sector, Beyond Limits is developing and deploying new products for refinery optimization, managing particulates in upstream wells, assisting chemical engineers in formulating lubricants products, and advising reservoir engineers on optimized well locations and in-fill well production rate prediction. As BP (a Beyond Limits customer and investor) recently stated, “Beyond Limits codification of human expert knowledge and use alongside machine learning has the potential to help the energy sector improve operational insight, improve safety, further optimize performance, and introduce additional process automation.” Ultimately, cognitive systems will function in coordination across business domains, fostering collaboration between experts in subsurface and drilling to improve accuracy and efficiency.

To help manage the operations of LNG power plants, especially in grids that need to balance supply and demand with a mix of intermittent power sources like solar and wind, Beyond Limits is developing a system for the worlds’ first power plant guided by Cognitive AI technology. The system will be installed in a new LNG plant to be constructed in West Africa to drive industrial development.

Three AI Use Cases from Oil & Gas with Potential for Geothermal Energy

Solution

These solutions combine machine learning data into the cognitive reasoner with reservoir engineering knowledge bases.

Result

The Beyond Limits system identifies high- productivity wells in physically meaningful reservoir areas in a matter of minutes to hours, rather than traditional approaches that can take days to weeks. The solution rapidly integrates new data and provides fresh recommendations, dramatically reducing cycle time while increasing the number of plausible development scenarios that may be investigated.

Problem

In oil and gas wells, high production rates can mobilize particulates into the production stream, damaging equipment over time, decreasing production, and increasing maintenance costs. Operators desired an AI monitoring solution that uses engineering knowledge and heuristics to predict or detect potential particle problems in advance, then recommend changes in operating conditions at the well-level and field-level to meet contractual production constraints and minimize particle production to ensure that volume targets are met.

Solution

Beyond Limits employs state-of-the-art machine learning and data science frameworks to evaluate risk associated with well performance such as particle production. Engineering knowledge bases are leveraged by our cognitive reasoner to determine production risks (as well as mechanism and volume levels) to suggest changes to improve operating conditions and efficiency.

Result

The Beyond Limits well health system detects major particle production problems with near 100% accuracy, which can significantly reduce downtime and operating costs. The technology also evaluates potential risks in future plans, enabling operators to leverage in-field experience for optimal well operation and production planning.

Problem

Every day, operations teams strive to manage systems, processes and team performance to optimize outcomes. Unfortunately, the operational state of a refinery often deviates from the assumptions used in the planning process and it requires human intervention and timely decisions to remediate throughput, quality, reliability and profitability risks and return the process to desired performance ranges.

Solution

Because refineries operate around the clock, Beyond Limits developed an always-on refinery operations advisory system. The cognitive decision support system empowers operations teams to improve operations plans through better transparency, communication and collaboration. The system stores, classifies, and leverages lessons learned so operators can uniformly apply best practices at the planning, operations, and management levels of the organization.

Result

Cognitive AI automatically identifies off- plan behavior. And, as data is collected in the system over time, it learns to better predict this behavior. Finally, the system provides guidance and remediation recommendations at the point of decision, based on optimization strategies and actions provided in the plan

We are in the early days of applying artificial intelligence to help manage and improve performance in oil and gas exploration and production. While there are parallels between oil and gas operations and Geothermal operations, AI systems have not yet been customized to meet the unique demands of the Geothermal industry. Given the strong potential evident in today’s new AI systems for energy production, it’s inevitable that machine learning and AI will become integral components of the Geothermal industry.

From a high level, artificial intelligence can be broken into two broad camps: Numeric AI (training from data), and Symbolic AI (education from knowledge). With conventional Numeric approaches, you train the system by providing example data (e.g., time series, images) of what you want it to classify. So, if you want it to recognize a cat, you supply it with thousands of different pictures of cats. With Symbolic approaches, you educate it by providing knowledge to understand the subject. So, if you want to create an AI doctor, you supply volumes of medical textbook knowledge that the system ingests.

For data-centric problems, numeric AI has accomplished great things in a variety of areas. However, conventional numeric AI machine learning and neural nets alone are insufficient to solve many complex problems where expert knowledge is a required component of reasoning and decision making, and when results need to be explainable, data is limited, unreliable, or misleading. On the other hand, symbolic reasoning alone is insufficient when data sets are large and clues from the data are needed to solve the problem.

• Brittle Solutions: Numeric approaches break when the same question is asked in a slightly different manner or the data changes just a little.
• Requires Pristine Data: When data is less than ideal, these systems do not fail gracefully but completely break down because they are unable to reason.
• Cannot Explain Answers: Artificial neural networks are statistically based, therefore their decision processes are not easily invertible. In other words, they are black boxes that cannot explain how they arrive at an answer.

For complex problems, AGI (Artificial General Intelligence) is the desired approach, but it is a long way off. Nevertheless, at Beyond Limits, we are working on getting there sooner than you might think.

Meanwhile, how can we solve complex problems today?

The newest generation of AI applications address high-value assets that require human-like understanding of complex domains with systems that can adapt to uncertainty in both their knowledge and data, supporting their answers with human-understandable audit trials (also known as Explainable AI or X-AI).

This requires the integration of different learning and adaptation techniques to overcome the limitations of the individual technologies and achieve synergetic effects through hybridization of symbolic and numeric technologies. At Beyond Limits, we have seen the need for a hybrid approach combining numeric machine learning technologies and symbolic reasoners for cognitive intelligence – the stepping-stone to AGI.

The machine learning technologies we use include artificial neural networks, deep learning, Supportive Vector Machines (SVMs), Bayesian networks, and genetic algorithms. Symbolic technologies include knowledge graphs, case- based reasoning and deductive, inductive, abductive, defeasible, and modal reasoning.

As we further develop our hybrid cognitive AI approaches combining the best of both worlds, the result will be self-organizing systems of AI- working-with-AI-working-with-Humans, which orchestrate intricate collaborations between specialized intelligent agents to solve highly complex problems requiring diverse shared expertise.