Enhanced Assessment of Fluid Saturation in the Wolfcamp Formation of the Permian Basin

Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. They also do not reliably assimilate the spatial distribution of the clay network and pore structure. Moreover, they do not incorporate other conductive minerals and organic matter, impacting the resistivity measurements and leading to uncertainty in water saturation assessment. We recently introduced a resistivity-based model that quantitatively assimilates the type and spatial distribution of all rock constituents to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to expand the application of this model for well-log-based assessment of water/hydrocarbon saturation and to verify the reliability of the introduced method in the Wolfcamp Formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and nonconductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we developed an inversion algorithm with two objectives: (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. The geometric model parameters are determined for each rock type or formation by minimizing the difference between the measured resistivity and the resistivity estimated from pore combination modeling. We applied the new method to two wells drilled in the Wolfcamp Formation of the Permian Basin. The formation-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 24.1% and 32.4% compared to Archie’s and Waxman-Smits models, respectively, in the Wolfcamp Formation. The most considerable improvement was observed in the Middle and the Lower Wolfcamp Formations, where the average clay concentration was relatively higher than the other zones. There was an additional 70,000 bbl/acre of hydrocarbon reserve using the proposed method compared to when water saturation was quantified using Archie’s model in the Permian Basin, which is a 33% relative improvement. It should be highlighted that the new method did not require any calibration effort using core water saturation measurements, which is a unique contribution of this rock-physics-based workflow.

Constructing the Resistivity-to-Sediment-Type Transform for the Interpretation of Airborne EM Data

A novel Markov Chain Monte Carlo (MCMC) based methodology was developed for the transformation of resistivity, derived from airborne electromagnetic (AEM) data, into sediment type. This methodology was developed and tested using AEM data and well sediment type and resistivity logs from Butte and Glenn Counties in the Californian Central Valley. Our methodology accounts for the spatially varying sensitivity of the AEM method by constructing different transforms separated based on the sensitivity of the AEM method. The large spatial separation that typically exists between the AEM data and the wells with sediment type logs was avoided by planning the acquisition of AEM data so as to fly as close as possible to the well locations. We had 55 locations with sediment type logs and AEM data separated by 100 m, determined to be the maximum acceptable separation distance. Differences in vertical resolution between the AEM method and the sediment type logs were addressed by modeling the physics of the AEM measurement, allowing for a comparison of field and AEM data generated during the MCMC process. The influence of saturation state was captured by creating one set of transforms for the region above the top of the saturated zone and another for below. Using the set of transforms developed at the 55 locations, an inverse distance weighting scheme that included a well quality ranking was used to construct a set of 12 (six sensitivity bins, and two saturation states) resistivity-to-sediment-type transforms at every AEM data location. These represent a set of transforms that accommodate the variation in AEM sensitivity and are independent of the inversion used to retrieve the resistivity model. These transforms thus avoid two of the significant limitations common to resistivity-to-sediment-type transforms used to interpret AEM data.

Andesite prospect at West Sungkai of North Lampung: Its distribution based on electrical resistivity tomography

Infrastructure construction made andesite’s demand has increased, particularly in Lampung Province. In this research, its distribution in West Sungkai of North Lampung is mapped based on Electrical Resistivity Tomography (ERT) data from 6 lines, each of them was 186 m in length. Due to its excellent vertical resolution, Wenner configuration is performed. The research area is part of Quarter Holocene Volcanic (Qhv) formation. Lajur Barisan members consist of volcanic breccia, lava, and andesite-basalt tuff; thus, resistivity modeling is built within this aisle. Subsurface resistivity model has been created using the non-linear inversion method with promising low error at the third to fifth iterations, which marks an acceptable value. Using 2D and 3D ERT modeling, it is estimated that there are three mains of rocks based on their resistivity value: sandy tuff with 65 – 212 Ω m; tuff with 212 – 655 Ω m; and andesite with resistivity more than 655 Ω m. Andesite within this area is likely lava andesite which spread from the middle to the West and north. It is located at 5 – 35 m in depths with the reserve estimation of andesite is about 1.65 million tons.

Geophysical Investigation of Groundwater Using Vertical Electrical Sounding in Mubi and Maiha Local Government Areas of Adamawa State, North Eastern Nigeria

This research work is aimed electrical resistivity survey for groundwater development conducted in Mubi and Maiha local government area of Adamawa State, in order to delineate the groundwater potential zones and determining the depth and thickness of sediments layers, and recommend suitable depth for drilling. Fourteen vertical electrical soundings (VES) were carried out within the study area using Schlumberger electrodes configuration was used for the field data acquisition. The field data obtained was analyzed using IX1D computer software and, VES1-14 resistivity model indicate 3-4 layered earth models. The interpretation shows positive inference in terms of a well-defined weathered basement and as such, it is likely to possess requisite hydro-geological characteristics that could supply underground water in fair quantity to well when drilled. Therefore, VES number denoted (R) are recommended for drilling at approximate depths of 40±5 to 50±5 meters.

Two-dimensional deep learning inversion of magnetotelluric sounding data

Conventional linear iterative methods for magnetotelluric sounding (MT) suffer from considerable limitations related to difficulties in selecting the initial model and local optima. On the other hand, conventional intelligent nonlinear methods exhibit slow convergence and low accuracy. In this study, we propose an inversion strategy based on the deep learning (DL) deep belief network (DBN) to realise the instantaneous inversion of MT data. A scaled momentum learning rate is introduced to improve the convergence performance of the restricted Boltzmann machine during the DBN pre-training stage, and a novel activation function (DSoft) is introduced to enhance the global optimisation capability during the DBN fine-tuning stage. To address the difficulty in designing the sample data when prior information is lacking, we employ the k-means++ algorithm to cluster the MT field data and use the clustering results as the prior information to guide the construction of the sample dataset. Then, based on the proposed DBN, we ensure end-to-end mapping directly from the apparent resistivity to the resistivity model. We implement two groups of experiments to demonstrate the validity of both improvements on the DBN. We consider six types of geoelectric model from the test set to demonstrate the feasibility and effectiveness of the proposed DBN method for MT 2D inversion, which we further compare with the well-known least-square regularisation method for several extended geoelectric models and field data. The qualitative and quantitative analyses show that the DL inversion method is promising as it can accurately delineate the subsurface structures and perform rapid inversion.

Geothermal resource exploration by using Numerical simulation and comprehensive geophysical method

Geothermal energy is an important renewable clean energy resource with high development and usage potential. Geothermal resources, on the other hand, are buried deep below, and mining hazards are significant. Geophysical investigation is frequently required to determine the depth and location of geothermal resources. The Transient Electromagnetic Method (TEM) and the Controlled Source Audio Frequency Magnetotellurics (CSAMT) have the highest detection efficiency and accuracy of all electromagnetic exploration methods. This article initially explains the algorithm theory of the finite difference technique before establishing a simplified geothermal system resistivity model. Established on the simplified resistivity model, a simulation analysis of the ability of CSAMT and TEM to distinguish target body faults at different resistivities and dip angles was performed, and the effectiveness and difference of the two methods in detecting typical geothermal resource targets was verified. A complete exploratory research of CSAMT and TEM was conducted in Huairen County, Shuozhou City, Shanxi Province, China, based on theoretical analysis. Both approaches can reflect the geoelectric structure of the survey region, demonstrating the efficacy of the two methods in detecting genuine geothermal resources.

Imaging of a serpentinite complex in the Kamuikotan Zone, northern Japan, from magnetotelluric soundings

We conducted magnetotelluric measurements to investigate a large serpentinite complex in the northern Kamuikotan Zone that intruded a Cretaceous–Paleocene forearc sedimentary sequence. The resistivity model we derived by three-dimensional inversion clearly shows a low-resistivity zone beneath the outcrop of the serpentinite complex. We interpret the low-resistivity zone to represent aqueous pore fluid within a serpentinite mélange derived from the subducting Pacific plate or mantle wedge. Previous geological studies in the area have shown that the serpentinite mélange had uplifted during the early Pleistocene. They indicate that the ultramafic rocks and aqueous fluids have continued to rise in the area. The uplifting serpentinite body might have formed a zone enriched in pore fluid that promoted the occurrence of a previously identified intra-plate slow slip event. These results demonstrate the important role of fluid transport during tectonic processes related to uplift in subduction zones.