Shale Engineering - My story
Offering a prognosis in unconventional “shale” requires a description of the host formation, the “artificial” reservoir, which is created by human intervention, and an understanding of the related physics. Formation properties and the earth stresses are conducive to establishing the artificial reservoir, which is induced by hydraulic stimulation. In a classic view of hydraulic fracturing, injected fluids permeate into the formation raising the pore pressure to the point that it counters the least of the principle stresses and gives form to a planar hydraulic fracture. In unconventional development this does not apply exactly.
The formations are not conducive to fluid permeation to their pores, instead the stresses imposed to the walls of a horizontal well can cause shear failure in the rock vicinity. Shear failure and the mechanical heterogeneities that exist (fractures, bedding planes, faults, etc..) create a complex network of fracturing exposing large surface areas. Injected fluids penetrate the formation that way and cause a gradual degradation of the formation to the point that it develops throughways of hydraulic fracturing. The mechanics of shearing and hydraulic fracturing are complicated and require the coupling of geomechanics to fluid mechanics in fractures and porous media. A simplified approach we proposed[1]in 2010 led to the adaptation of dilation curves into reservoir simulation models, which are capable of describing the gradual generation of the artificial reservoir during hydraulic stimulation and the flow behavior afterwards.
This approach was validated when used in a pilot project, which offered rich petrophysical, microseismic, and production surveillance. In that project the role of proppant was isolated as it was found by the operators that it was necessary, and the study really shed light to the nature of complex fracturing mechanics. It was successful in constraining the uncertainties in production predictions providing a reliable projection, which was later found to coincide with the type curve average of more than 3,000 similar wells. As the shale engineering became a tool in offering predictions in places that it was not possible otherwise (like in China, Europe, etc..) it also became a workbench of optimizing development and completion practices.
The latter was and is the focus of NITEC LLC as their founder, Chet Ozgen , came to a parallel conclusion independently. During my tenure at Repsol, I had a chance to employ Nitec’s services and that process has led us to a great turnaround of a very challenging play. Presently after leaving Repsol, I founded Nitec Pegasus LLC in partnership with Mr. Ozgen in order to promote the use of shale engineering beyond the asset level, which is the typical Nitec clientele, to the planning and strategic level, which would eventually conform to the principles of modular development.
The main takeaways from this work was the discovery of parent-child interference before that became known in the industry by field results. As an example, in the Mississippi Lime play c 2014 we were able to identify the problem through modeling and field observations from wells that were close enough in space, but further apart in time. At that time the well behavior was consider erratic, but not uncommon in the grand development scheme.
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Our team was able to recognize this issue and to design around this implication with co-development and even with multilateral well designs. It took the rest of the industry 3-5 years to reach the similar understanding by analyzing well behavior from thousands of unconventional wells in many different basins. So far the industry has adopted cube co-development as a remedy to parent-child interference, and it remains to be seen when multilateral wells will make an entry in the Permian and other prominent plays.
Companies that want to make profit in shale plays and want to achieve organic growth need to use shale engineering. Similarly shale engineering has helped starting the shale revolution in new basins and new countries where no type curves are available. In these cases first-principle predictions proved reliable and set the stage for new tight oil and gas development in Europe, China and other regions.
[1] George D. Vassilellis, Charles Li, Vivian K. Bust/ Gaffney, Cline and Associates; Daniel Moos and Randal Cade, “Shale Engineering Application: The MAL-145 Project in West Virginia”, SPE 146912, presented, Canadian Unconventional Resources Conference held in Calgary, Alberta, Canada, 15–17 November 2011.
Daniel Moos,? G. Vassilellis,? R. Cade, J. Franquet, Baker Hughes; A. Lacazette, Bourtembourg, G. Daniel, “Predicting Shale Reservoir Response to Stimulation in the Upper Devonian of West Virginia”, SPE 145849, Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 30 October–2 November 2011.
George Vassilellis, Chao Li, Rawdon Seager, Daniel Moos, “Investigating the Expected Long-Term Production Performance of Shale Reservoirs”, CSUG/SPE 138134, Calgary, Alberta, Canada, October 20th, 2010.
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Chief AI Officer | Optimized $50B+ with Predictive AI | On a mission to help teams succeed with AI
12 个月In addition to well spacing, also critical to model time offset when adding child wells to a DSU, which Amrita Sen, Rahul Verma, Yao You, and others did extensive work in at OAG Analytics.
M.S., P.E.
1 年George, in regard to your original questions, please scan the recent 'Shale Oil & Gas Criteria and Cutoffs" book for some quick-look, pre-drill, production forecasting techniques for shale. Also covered are the societal costs and benefits of development, and a method for roughly predicting frac behavior prior to a treatment.
Licensed Petroleum Geologist- Global New Ventures-Senior Advisor
1 年A game we have played for 15 years.
CEO of ResFrac Corporation
1 年If stimulation in shale creates volumetric networks of shear stimulating fractures, why do core throughs keep showing sub parallel swarms of hydraulic fractures? The conceptual model that you are expressing was popular in the early days of shale, but seems to have been supplanted as we have gathered higher quality data, like from offset fiber and core.
Decision Intelligence, Strategy, Training, Project Risk Management, Earth Science, Medic
1 年You can make decisions based on what you know from analogues but there will be uncertainty. As you explore for a viable business case, you are really trying to define the family of wells that will be found (see the SPE paper Jeff Brown and I wrote). Well density/spacing is dependent on several different things and a primary recognition will be that your spacing decision has asymmetric risk.