The Use of Advanced Downhole Geophysical Tools for Detailed Aquifer Characterization. By Shawky, I., Labaky, W. and Delhomme, J.P.

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1 The Use of Advanced Downhole Geophysical Tools for Detailed Aquifer Characterization Abstract By Shawky, I., Labaky, W. and Delhomme, J.P. Aquifer storage and recovery (ASR), passive groundwater remediation and source containment are all but a few examples of current challenges in hydrogeology. One essential prerequisite for success in all of the above activities is the need for accurate and detailed site characterizations. The scale and aerial extent of the required characterization usually varies with the nature of the problem and the properties of the aquifer under investigation. The oil exploration industry has a large variety of technologies for reservoir characterization. Combinable Magnetic Resonance (CMR), Fullbore Formation Micro Imager (FMI) and Elemental Capture Spectroscopy (ECS) are some of the tools available that can assist in quantifying parameters such as porosity, permeability, pore distribution and type of pore fluids. Such technologies are increasingly being applied in groundwater studies where advanced hydrogeologic characterization is required. The above technologies have been used successfully at a real site in parallel with Gamma Ray, Compensated Neutron Log, Three detector Lithology Density, Array Induction Tool and Caliper Logging. The investigation was conducted within the context of a larger study to assess the feasibility of ASR at the site. The highly detailed characterization allowed the precise delineation of targeted layers and the determination of their hydrogeologic properties thanks to new quantitative methods of interpretation. The derived information was essential to the development of the conceptual hydrogeologic model for the site. Keywords: Aquifer Characterization, Aquifer Storage and Recovery, Fullbore Formation Micro Imager, Groundwater Remediation, Hydrogeology, Combinable Magnetic Resonance. Introduction Aquifer characterization is essential for the successful implementation of numerous hydrogeologic activities, namely Aquifer Storage and Recovery (ASR). Critical aquifer parameters need to be evaluated with a high degree of accuracy at early stages for the construction and running of reliable predictive simulators. Examples of the critical parameters include: aquifer thickness, aerial extension, lithology, porosity, water salinity, hydraulic conductivity and pressure. Aeolian and Fluvial deposits can display a high variability in grain size distribution. Consequently, the volume of movable water, or in other words, the available storage capacity of the aquifer will also vary. Traditional logging and log interpretation techniques are only able to give a total porosity lumping together movable and nonmovable water.

2 The present paper will present the applications of these techniques within the framework of a Schlumberger ASR project in the Middle East. An integrated assessment of the individual methods was used for the construction of a high resolution static model, populated with measured aquifer rock properties, such as lithology, effective porosity, and hydraulic conductivity. Theory The Combinable Magnetic Resonance (CMR) The CMR tool makes nuclear magnetic resonance (NMR) measurements that respond to the hydrogen nuclei contained in pore fluids, giving information on pore dimensions. The magnetic moment and angular momentum of the hydrogen nuclei cause them to behave like bar magnet and gyroscope combinations. The nuclei tend to align in the magnetic fields produced by permanent magnet and radio frequency (rf) pulses. However, the alignment process is resisted by the angular momentum of the nuclei, which results in a processional motion analogous to the wobbling motion of a toy top spinning in the earth s gravity field. Figure 1. Amplitude of a CMR measurement as a function of time. The initial signal amplitude (Figure 1) is proportional to the number of hydrogen nuclei in the measurement volume that are associated with the pore fluids, thus it can be calibrated to give a total porosity. The signal amplitude decays exponentially with time, the time constant of the signal decay is called the transverse relaxation time T2. T2 has been shown to be proportional to the pore size: small pores have shorter T2 values, and large pores have long T2 values (Figures 2a and 2b).. At any depth in the well bore, the CMR measurement will have a distribution of pore size, and the area under the T2 distribution curve is equal to the measured porosity. Porosity and pore size information from the CMR measurement are used to estimate both percentage of movable water and permeability The CMR estimate of movable water is referred to as the free fluid porosity, and is based on the expectation that the movable water resides in the large pores, whereas the bound water resides in the smaller pores. Thus a cutoff can be applied to the T2 distribution that divides the CMR porosity into free-fluid and bound-fluid porosity. The CMR estimate of permeability is similarly based on the expectation that it will increase with both porosity and pore size, and can be estimated as: KCMR = a (ΦCMR) 4 (T2,log) 2

3 Figure 2a. T2 distribution Figure 2b. Fluid distribution in large and small pores Elemental Capture Spectroscopy (ECS) The ECS probe, uses a standard AmBe neutron source and a GR crystal for measuring relative elemental yield based on a neutron-induced capture gamma-ray spectroscopy. The primary elements measured with the ECS include Si, Ca, Fe, S, Ti, Gd, Gl, and H (Figure 3). The silicon, Calcium and Iron elements concentrations are used to estimate the clay volume, while the carbonate volume is determined from the calcium concentration log, Anhydrite is determined from calcium and sulfur concentrations, finally, the reminder of formation is composed of Quartz, Feldspar, and Mica minerals. n Silic Calci Iron Sulf Titani Gadolin Oxides Capture Spectra Si Relative Yields Closure Ca 100 Fe 120 S Elemental Standards Elemental Concentrations Lithology Figure 3. ECS measurements: from elemental to lithologic interpretation.

4 Formation Micro Imager (FMI) The Formation Micro Imager tool allows continuous observation of detailed vertical and lateral variations in formation properties. During logging, several microelectrodes within the tool emit a focused current into the formation. The button current intensity measurement, which reflects microresistivity variations, is converted into variableintensity color images. The processing of electrical currents recorded by the micro electrodes provides images which look like core photographs. The observations and analysis of the images provide information which can be related to changes in rock composition and texture, structure, or fluid content. Results and Interpretation 12 CMR borehole geophysical logs were acquired across the aquifer and a detailed core analysis was carried out at one of these wells. The CMR logs were used to determine porosity (total, effective, clay and capillary bound), pore size distribution and hydraulic conductivity. The measured values were compared to the Core Analysis results. Figure 4, illustrates the comparison between the CMR log, and the core analysis results, on top right hand side, a thin section showing moderately well sorted grain sizes, and the histogram below it shows grain size distribution being dominated by fine grains. On top left hand side the CMR pore size distribution percentiles are shown: red is coarse, yellow medium, blue is fine, grey is very fine, brown and pink are clay size. A good agreement between the core and log measurements was observed. Mercury injection measurements and grain size analyses were carried out. We notice on the T2 distribution that an appreciable percentage of the distribution falls above the T2 cutoff, meaning a good percentage of the water is free to move, in this case the free water porosity is 27% compared to a total porosity in the range of 33% to 35%., unlike other traditional porosity measurements such as Density, Neutron, and Acoustic methods that measure the total porosity resulting in overly optimistic model, CMR is the only measurement which will measure the effective porosity

5 Thin Section Figure 4. Comparison between CMR and core analyses Figure 5, shows on the left hand side, the CMR log and on the right hand side, the relationship between log and core porosity on top, and log and core permeability at the bottom. On the left hand side track the formation permeability is in continuous blue line, and core permeability in green and red dots, showing a good agreement between the two independent measurements. The middle track shows the porosity measurements where three porosities were defined: Total Porosity, represented with black solid line, and includes the percentage of all waters in the rock to the total volume. Free Fluid porosity, represented by the pink line, and is a measure of the amount of water free to move under differential pressure. Dashed black curve represents Total Porosity minus Clay Bound water, the difference between this one, and the free fluid porosity is the capillary bound water. On the cross plot, on the upper right hand side, between log and core porosity, it is worth mentioning that Total porosity has been taken as log porosity, as the core was oven dried before porosity measurements, and thus lost all its water including the clay bound water.

6 CMR Porosity Vs Core Porosity C M R T o t a l P o r o s i t y y = x Core Porosity (1) (2 (3) (4) (5) y = x CMR Vs Core Perm (12 (6) (7) (8) (9) (10 (11 C M R P e r m Core Perm. Figure 5. CMR logs and log and porosity and log and permeability relationships Elemental Capture Spectroscopy (ECS) 6 wells were logged with ECS and were compared with dry weight elemental concentration on core analyses using X-ray diffraction. Figure 6 shows the ECS log together with the elements concentrations. On the left hand side the ECS log is presented with the core analysis results superimposed on the log curves, the clay volume percentage is colored gray, the carbonate light blue, and the combination of quartz, feldspar and mica minerals are colored yellow, while the total carbonate from the core analysis are represented by black dots, which is the summation of the calcite (blue dots, and dolomite (pink dots). On the right hand side of the log, the dry weight elemental concentrations of the different elements are presented. The results generally indicate a good agreement between the ECS and the X-ray diffraction measurements. ECS interpretation showed zero carbonate content at certain depths intervals within the aquifer, which is confirmed by the tabulated core plug analysis showing both calcite and dolomite concentration of less than 1% each. A gradual increase in both the clay minerals and carbonate cementations indicate the bottom of the aquifer, which was confirmed by the CMR permeability measurement.

7 Elemental Capture Spectroscopy Figure 6. ECS vs. X-ray diffraction Whole Rock Mineralogy as Determined by X-Ray Diffraction (Weight Percent) Formation Micro Imager (FMI) 7 wells were logged with FMI and the results were used for structural and sedimentary interpretations. The amount and direction of the dip of the different layers can be identified. The sedimentary analysis refers to the megascopic morphological features, which are often visible to the naked eye. These include the thickness and shape of the beds, their internal organization, the nature of their surface, joints, and fossil contents. The importance of sedimentary structures stems from the fact that they reflect the hydrodynamic conditions at the time of deposition. FMI measurement, having such high vertical resolution, in the range of millimeters, has the advantage that it capable of detecting very thin effects which can be related to sedimentary features. The aquifer petrophysical properties from the above measurements (CMR, and ECS) were combined with aquifer rock bulk density, neutron porosity and resistivity and entered into a simultaneous solver to produce the aquifer parameters needed to populate the static model. The petrophysical model thus obtained from the logs and analyses, together with the FMI structural, sedimentary, and texture information are loaded to the static model at the well locations and along their trajectories. Figure 7 shows the free water porosity with the color coding going from pink for zero porosity to red for 30% porosity.

8 Figure 7. Free water porosities in wells And similarly all the other aquifer properties, deduced from the log runs, are loaded to the corresponding well trajectory. Figure 8 shows the up-scaled Free Water Porosity for few of the wells in the project area. Figure 8. Free up-scaled porosities Up-scaled free water porosity Finally, these up scaled petrophysical values for each cell along the well trajectory are interpolated between the wells in the 3D grid, which results in a grid with property value for each cell, producing a high resolution, high accuracy geological/petrophysical model. Kriging deterministic population method including variogram information was used to produce an anisotropic model capturing the geostatistical dependencies between points in

9 the 3D grid. Figure 9 shows the high resolution property populated static model ready to load into the dynamic simulator. Figure 9. High resolution 3D properties model Conclusions The use of advanced geophysical methods has led to the detailed characterization of the aquifer. CMR logging determined parameters such as correct porosity (free water) porosity, hydraulic conductivity. Moreover, an accurate delineation of the aquifer bottom, the transition zone between the saturated and unsaturated media and the water table, was obtained. ECS logging was used for the determination of the elemental mineral composition of the aquifer and the results were corroborated by X-ray diffraction analyses on core samples in the laboratory. FMI was used for the structural and sedimentary characterization and the data from different methods and in individual wells were interpolated across the aquifer to develop a static conceptual model of the aquifer as a first step towards the predictive modeling of the dynamic behavior.