Electromagnetic Methods for Geophysical Characterisation
Investigators: Jon Saunders, Murtaza Gulamali, Matt Jackson, Chris Pain, Jan Vinogradov, Eli Leinov
Electrical resistivity inversion
A 2D resistivity inversion example using structural constraints
Electrical Resistivity Inversion aims to gain information about a sub-surface by finding the spacial and/or directional dependence of electrical resistivity throughout the domain. Typically, potential is measured between many pairs of electrodes positioned so as to offer good coverage of the region under investigation. These electrodes could be positioned on the earth surface or in boreholes drilled into the formation. The inversion algorithm then proceeds to find the resistivity profile which best reproduces the measured data. This method is commonly used to map fracturing in rock, a feature which, by its very nature, introduces anisotropy into the resistivity profile of the sub-surface. Measurements taken along the veins of a highly conductive fluid-filled fracture network will be significantly different from others taken across the fracturing where the majority of the path is through resistive rock. To this end we are developing software which in the most general case can perform 3D anisotropic resistivity inversion. Synthetic tests have shown a high degree of accuracy, especially when structural constraints are added to enforce smoothness or allow detail to develop depending on the curvature of the target image. For inversion of field data where target images are not available, seismic velocity and anisotropy data is used to construct the constraints to guide the inversion. Such constraints reduce the level of underdetermination in the inversion algorithm and so allow a greater degree of confidence in the resulting conductivity profile.
An animation showing results of 3D constrained resistivity inversion of a domain containing two intersecting fractures:
Characterisation using self-potentials
Electrical methods for characterisation of groundwater systems and hydrocarbon reservoirs are not only constrained to active experiments involving injection of current, as described above. A naturally occuring phenomenon can also be employed to make passive measurements for the same purposes. Self-potentials are naturally occurring electrical potentials, and can contain contributions from three distinct phenomena:
electrokinetic potentials occur in response to gradients in pressure and the resulting generation of electrical current by relative movement of fluid and solid phases. The reaction of the pore fluid with the mineral surface creates an excess of charge at the fluid-solid interface. Fluid moving through the pores carries some of this excess charge along with it, creating an electric current in the direction of the fluid motion. This is balanced by a conductive current, and the potential required to maintain this current is known as the electrokinetic or streaming potential.
electrochemical potentials result from gradients in chemical composition, down which ionic species migrate. The differing mobilities of the constituent ions leads to charge separation, which is countered by the electrochemical potential in order to maintain overall electrical neutrality.
thermoelectric potentials arise from gradients in temperature and, similar to the electrochemical potentials, are due to ionic charge separation as ions migrate along the temperature gradient.
The principal component is typically the electrochemical potential, but if pressure gradients are imposed to move the pore fluids the electrokinetic component can dominate, depending on the composition of materials.
The first part of our work focuses on the electrokinetic potential, and is concerned with identification of the fluids in a hydrocarbon reservoir and their distributions during production. The parameter known as the streaming potential coupling coefficient, which links together the dynamic and electrical currents, is strongly dependent on the chemistry of the fluid. We use this fact to identify the fluid in the near vicinity of a producing well, monitoring the potential readings to watch for the arrival of the water which replaces the hydrocarbons in the reservoir.
The second ongoing part of our investigations into the capabilities of electrokinetic phenomena is in characterisation of formation permeability round a borehole. Permeability is one of the hardest properties of formation rock to calculate in situ, but is crucial for the evaluation of a hydrocarbon reservoir as permeability controls how easy it would be to extract the oil or gas. Our method involves placing an acoustic source in the borehole, long with a number of electrical detectors up to a few metres from the source. The acoustic waves generated by the source cause the fluid to move through the pores in the surrounding rock formation. As described above, this relative movement will generate a streaming potential which can be measured at the borehole electrodes. For example, the Stoneley wave which propagates along the borehole will be affected by fractures in the rock, losing energy quickly if there is a large amount of fracturing, and thus high permeability. Investigations are currently underway to understand this process better.
An animation showing the response (displacement) to an acoustic source positioned in a borehole surrounded by an elastic porous medium. Compressional and shear waves propogate through the porous medium, while the Stoneley wave travels slowly up the borehole. Symmetry axes left and bottom:
Publications
Saunders JH, Jackson MD, Gulamali MY and Pain CC, Streaming potentials in reservoir conditions, Geophysics (in review).
Jackson MD, Butler AP, Vinogradov J, Eastgate T, Awe O and Saunders JH, Measurements of spontaneous potential in chalk with application to aquifer characterisation in the southern UK, Q. J. Eng. Geol. Hydrogeol. (in review).
Jackson MD, Vinogradov J, Saunders JH and Jaafar MZ, Laboratory measurements and numerical modeling of streaming potential for downhole monitoring in intelligent wells, Soc. of Pet. Eng. J. (in press).
Ardjmandpour N, Pain C, Singer J, Saunders J, Aristodemou E, Carter J, Artificial Neural Network Forward Modelling and Inversion of Electrokinetic logging Data, Geophysical Prospecting, Accepted Oct 2010, In press.
Jackson MD, Vinogradov J, Saunders JH, Laboratory Measurements and Numerical Modeling of Streaming Potential for Downhole Monitoring in Intelligent Wells, SPE, Accepted Oct 2010, In press.
Jaafar MZ, Vinogradov J, Jackson MD, Saunders JH, Pain CC, Measurements of streaming potential for downhole monitoring in intelligent wells, SPE, 2009, Vol:120460
Saunders JH, Jackson MD, Pain CC, Fluid flow monitoring in oilfields using downhole measurements of electrokinetic potential, Geophysics, 2008, Vol:73, Pages:E165-E180, ISSN:0016-8033
Singer J, Saunders JH, Holloway L, Stoll JB, Pain C, Stuart-Bruges W, Mason G, Electrokinetic logging has the potential to measure permeability, Petrophysics, 2006, Vol:47, Pages:427-441, ISSN:1529-9074
Saunders JH, Jackson MD, Pain CC, A new numerical model of electrokinetic potential response during hydrocarbon recovery, Geophysical Research Letters, 2006, Vol:33, ISSN:0094-8276
Pain CC, Saunders JH, Worthington MH, Singer JM, Stuart-Bruges W, Mason G, Goddard A, A mixed finite-element method for solving the poroelastic Biot equations with electrokinetic coupling, Geophysical Journal International, 2005, Vol:160, Pages:592-608, ISSN:0956-540X
Jackson MD, Saunders JH, Addiego-Guevara EA, Development and application of new downhole technology to detect water encroachment towards intelligent wells, SPE, 2005, Vol:97063
