Groundwater Pathway Mapping Using Electrical Resistivity Tomography (ERT) Method

Geophysical electrical resistivity method has been one of the more popular non-destructive method to explore the subsurface. Geophysical electrical resistivity tomography (ERT) subsurface profiling was conducted to map the groundwater path along the embankment. The groundwater path able to decrease the slope stability, thus its need to locate the position for conduct the slope remediation via subsoil drainage. In this study, Terrameter LS2 model, electrodes, cables, battery, and cable connectors were the equipment used for measurement. This study uses cable spread line at 200m with 2.5m spacing between electrodes by using gradient protocol. The resistivity data was analyzed using RES2DINV software. The interpretation of groundwater path is based on the resistivity values less than 100 ohm.m, which is interpreted as saturated materials. This study demonstrates the efficiency of application of electrical resistivity tomography (ERT) in detecting the groundwater pathways. This investigation will help in sustaining the slope stability via indicating the position of groundwater pathways, and thus implementing the slope remediation work.

Designing Aquifer Model for the Banks of the Serayu River, Sokawera, Somagede, Banyumas, Indonesia by Means of 1D-Electrical Resistivity Data

A geoelectric survey using the 1D-electrical resistivity method was applied to design a groundwater aquifer model for the banks of the Serayu River in Sokawera Village, Somagede District, Banyumas Regency, Indonesia. The aim of this research was to identify the characteristics of aquifers in the research area based on resistivity log data. Acquisition, modeling, and interpretation of resistivity data were carried out and the results were lithological logs at seven sounding points. Correlation between the lithological logs resulted in a hydrostratigraphic model. This model is composed of several hydrological units, i.e. shallow aquifer, aquitard, and deep aquifer. The shallow aquifers are composed of sandy clay (10.81-18.21 Wm) and clayey sand (3.04-7.43 Wm) with a depth of groundwater from the water table to 27.51 m. The deep aquifers are composed of sandstone with variation of porosity (2.24-12.04 Wm) at a depth of more than 54.98 m. Based on this model, potential shallow aquifers were estimated to be at sounding points Sch-5, Sch-6, and Sch-7. This hydrostratigraphic model shows that the two types of aquifers are separated by an aquitard layer, allowing groundwater infiltration from the shallow aquifer to the deep aquifer and vice versa. Moreover, the Serayu riverbanks in this research area are estimated to be a groundwater discharge area.

Seepage Patterns in an Earth-Rock Fill Dam Evaluation using Electrical Resistivity Tomography (ERT) Method

Seepages in the earth-rock fill dam are usually monitored by pore pressure, seepage water table, and seepage discharge. However, those monitoring are difficult to describe the seepage patterns because they are installed only in certain points. This research evaluated the seepage pattern resulting from Electrical Resistivity Tomography (ERT). The resistivity was measured by installing electrodes upstream of the Dam at every 10 m and downstream at 20 m distances. The seepage pattern was analysed from the resistivity 2 Dimension distribution using the RES2DINV program. The results showed that the seepage pattern resulting from the ERT method’s resistivity data, which was compared with data of surface dam deformation, pore pressure, and seepage water table, could explain the seepage discharge data. Based on those confirming data, the resistivity data of the ERT method was appropriate to explain the seepage pattern in the earth-rock fill dam and can be further utilized for dam stability analysis.

Application of GSTAR(1,1) model for layer peat soil predicted based on resistivity log data

In this study, GSTAR modeling was carried out with the inverse of distance weight matrix obtained from Geoelectrical Resistivity data at several peatland locations around the Universitas Tanjungpura, Pontianak. This data can identify the subsurface layer of the soil through the electric current that binds into the soil. However, due to the limitation of the tool to measure the resistivity value, it can only measure 1/5 of the depth of the observation length. To overcome this problem, predictions are made at the next depth using the GSTAR model. The study began by measuring the resistivity value of the land using the geoelectric method and mapping it. Through this GSTAR modeling, predictions are made for the unobserved subsurface to determine the type of soil layer. Knowing the type of deeper soil layer can help contractors build plant concrete stakes to keep buildings safe on peatland. The results of the GSTAR(1.1) model are not accurate enough to estimate the resistivity value data. This is possible because the correlation between rock ages is not the same, so further analysis is required.

Implementation of Vendor-Independent Stochastic Inversion for Improving Quality and Efficiency of Well Placement on the Field of Novatek Company

The article describes the application of a new stochastic inversion of the deep-azimuthal resistivity data, independent from the tool vendor. The new model was performed on the data from several wells of the PAO «Novatek», that were drilled using deep-azimuthal resistivity tools of two service companies represented in the global oilfield services market. This technology allows to respond in a timely manner when the well approaches the boundaries with contrasting resistivity properties and to avoid exit to unproductive zones. Nowadays, the azimuthal resistivity data is the method with the highest penetration depth for the geosteering in real time. Stochastic inversion is a special mathematical algorithm based on the statistical Monte Carlo method to process the readings of resistivity while drilling in real time and provide a geoelectrical model for making informed decisions when placing horizontal and deviated wells. Until recently, there was no unified approach to calculate stochastic inversion, which allows to perform calculations for various tools. Deep-azimuthal resistivity logging tool vendors have developed their own approaches. This article presents a method for calculating stochastic inversion. This approach was never applied for this kind of azimuthal resistivity data. Additionally, it does not depend on the tool vendor, therefore, allows to compare the data from various tools using a single approach.

Geo-Electrical Method of Assessment of Coefficient of Permeability and Porosity of Soil of a Poultry Farmland

Leachate collected at the bottom of dead bird’s disposal pits may leak and migrate to pollute groundwater when soils and rocks present are porous. This study assessed the coefficient of permeability (K) and porosity (Ф) of soils and rocks in poultry farmland using 2 Dimensional (2D) electrical resistivity method and soil analysis. Geo-electrical data collection was achieved by using the dipole-dipole array. The field resistivity measurement was carried out along three traverse lines (three Profiles) of 100 m long which were oriented along with East-West directions. These measurements were taken in the order of increasing in offset distance interval of 5 m. The acquired apparent resistivity data were inverted using DIPPROWIN modeling software to perform 2D data inversion. Five soil samples from different locations at depths of 0 – 15 cm and 15 – 30 cm, on the poultry farmland, were collected, transported, and tested in the laboratory. K and Ф were determined using falling head and density methods respectively. The results obtained from the processed field resistivity data from the three profiles were presented as field data pseudo-sections, theoretical pseudo-section, and 2D resistivity structures. The 2D resistivity structure revealed three structures viz; highly conductive, slightly conductive, and resistive. The resistivity values of these structures ranged from 14.1-99.0 Ω m, 100-848 Ω m, and 1350-90330 Ω m respectively. The highly conductive structures were found in profiles 1 and 3 due to the downward migration of the contaminants from the dead bird disposal pit 1 and the feces disposal site through clayey sand soil. This occurs at the depth range of few meters from the surface to greater than 20 m. The presence of the slightly conductive structure is a result of filtration of the contaminants by the soil materials which increased the resistivity of the soil. The movement of the contaminant through the soil is an indication of the porous and permeable nature of the farmland. The resistive structure is only noticeable in profiles 1 and 2 but very prominent at the depth range of 5 m to more than 20 m and 5 m to 35 m along the profile length. The results of the analysis of the five soil samples from the poultry farmland showed a high value of 0.552 and 3.554 x 10−2cm/s of porosity (Ф) and coefficient of permeability (K) respectively. A strong correlation of R 2 = 0.9878 existed between Ф and K. With these results geo-electrical method had successfully assessed Ф and K of the soil of the poultry farmland.