Novel detection system for NMR logging tool

A new pulsed nuclear magnetic resonance (NMR) logging tool, known as the CMR* Combinable Magnetic Resonance tool, is being used worldwide. During the CMR tool development phase, one challenge was to design a robust and economical data acquisition and signal processing scheme for the hundreds or thousands of spin-echo amplitudes that can be acquired during the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. This challenge was met by developing a new signal processing and associated downhole data compression algorithm. Data compression is essential to reducing the processing times so that formation T2-distributions can be estimated in real time. Compression of the digital data is possible, without loss of information, because the linear dependency of the NMR measurement kernels results in gross redundancy of the measured spin-echo amplitudes. An attractive feature of the algorithm described in this paper is that the compression can be performed in the downhole tool, thus substantially reducing the telemetry requirements. The raw spin echoes can also be sent uphole and made available for additional processing. Logs of CMR porosity, free-fluid porosity, mean relaxation time and rock permeability are computed from the estimated T2-distributions. The accuracy and precision of the CMR log outputs are demonstrated by repetitive Monte Carlo computer simulations in which noisy, synthetic spin-echo amplitudes are generated from known T2-distributions and then processed to obtain log outputs. Monte Carlo simulations are used to elucidate CMR log responses in typical clean sand, shaly sand, and carbonate rocks. The relative insensitivity of the measurements to short relaxation times (e.g., those less than a few milliseconds) is discussed and used to explain the differences between CMR log porosity and "total" formation porosity in shaly formations. We show that CMR porosity is an "effective porosity" that does not include clay bound water and microporosity having relaxation times less than a few milliseconds. We give examples of statistical fluctuations that can occur on estimated T2-distributions to assist log analysts in recognizing artifacts that are not indicative of actual reservoir rock properties. Field logs that display many of the features of CMR log responses revealed by the simulations are also presented.

This paper presents results of laboratory studies of nuclear magnetic resonance (NMR) relaxometry for NMR log calibration aimed at enhancing petrophysical measurements in oil wells. Relationships between NMR longitudinal relaxation parameters and the Hydraulic Units (HU) concept for predicting petrophysical properties from wireline logs have been established previously. However, modern NMR logging tools measure the transverse relaxation parameters. Hence in this paper, we show that measurements of the transverse component of the NMR signal can provide the same information as the longitudinal. Laboratory NMR transverse relaxation characteristics of water in rock samples were measured with CoreSpec-1000. The NMR data were used to validate models for predicting permeability and producible fluid in clastic rocks. The predictive model for permeability includes the rock surface area to grain volume (Sgv) the tortuosity of the flow paths (), the shape factor (Fs), the transverse relaxation time (T2), relaxivity (), and porosity (). The models were used to develop the required NMR log calibration. The transverse relaxation parameters were used with core analysis data of permeability, porosity, and mercury injection capillary pressures to sort the rock samples into hydraulic (flow) units. Protocol for the use of laboratory NMR core spectrometry and core analysis data for NMR log calibration is presented. Relationships between T1 and T2, and between flow zone indicators (FZI) and relaxivity () were established. It was observed that T2 cut-off (T2c) is related to T1 cut-off (T1c) in the same way that T2 is related to T1. Relaxation time cutoffs are used for determining producible fluid (free fluid porosity or free fluid index, FFI). The results show that reliable prediction of permeability and producible fluid from NMR logging tools requires calibration of the tools with laboratory NMR measurements on representative rock samples. We demonstrate the use of T2c for determining FFI from T2 distributions. The methodology for identification and characterization of HU within geological facies based on NMR T2 was validated. Introduction The (HU) concept has successfully been integrated with laboratory core NMR measurements for evaluating petrophysical properties from wireline logs (Ohen et al.). The HU and NMR techniques were linked through the FZI and NMR relaxation parameters, because both the FZI and the relaxation parameters are related to the rock surface phenomenon which controls the microscopic attributes of the rock. Ohen et al. measured the longitudinal (spin-lattice) relaxation time (T1) on a number of reservoir rock samples at a Larmor frequency of 1 MHz with the CoreSpec-1000. P. 329

Obtaining the information of shallow surface hydrogeological parameters is of great significance to the study of the groundwater cycle, the development and utilization of groundwater, and the protection of groundwater. Nuclear magnetic resonance (NMR) technology is the only method among all geophysical methods that can directly qualitatively and quantitatively measure groundwater content and evaluate aquifer reservoirs. The emergence of small-diameter NMR logging tools provides a powerful tool for the evaluation of shallow surface hydrogeological parameters. However, the low signal-to-noise ratio (SNR) of the NMR signal is still an important factor restricting the development of such instruments. In this article, we propose a design scheme for the receiving system of a small-diameter NMR logging tool. Its main design purpose is to improve the SNR of the receiving signal. The receiving system mainly includes an integrated differential coil, MOSFET-based duplexer, series LCR signal processing circuit, and ultralow noise lithium battery power. To verify the effect of the receiving system we designed, a sand sample moisture content test experiment has been conducted. The experimental results show that the NMR logging system can measure the water content accurately and reflect the distribution of porosity of the sample. The comparison with other small-diameter NMR logging systems shows that the receiving system we designed has a good receiving effect and is close to the level of commercial equipment. Finally, we expect such instruments to play an important role in the evaluation of pollution on the shallow surface.

Transverse relaxation T2 spectrum obtained by nuclear magnetic resonance (NMR) logging tools is an intuitive reflection of the pore size distribution for subsurface formation, which is valuable for petroleum reservoir characterization. However, the deployment of NMR logging tools is constrained by financial and operational factors, while NMR data are only available in very limited wells. This seriously limits its application in practices. Therefore, researchers try to synthesize NMR T2 spectra from more widely measured conventional logging data with the help of machine learning technologies. In the article, we propose the spectrum regression forest (SRF) algorithm for the prediction of NMR T2 spectra from conventional logging responses. Based on the experiment on the real-world well data of carbonate reservoir, the proposed algorithm is proved to provide effective NMR T2 spectrum predictions with accuracy amplitudes and consist morphology, which is believed to enhance the understanding of formation pore structures for future reservoir characterization practices.

Samaria Tertiary field in southeastern Mexico produces extra-heavy to medium grade oil (5 to 23° API) from sand-shale sequences. It is well known that the nuclear magnetic resonance (NMR) logging application can provide the total and effective porosity values, as well as free and related fluids independent of mineralogy. When the NMR logging tool is run in heavy oil deposits, oils of varying viscosities can be distinguished qualitatively. Unfortunately, however, in the presence of heavy oils, this measurement process underestimates the total porosity and effective porosity, and overestimates the bulk volume irreducible water (a key parameter for estimating permeability). Although the literature specifies methods that can correct this porosity and fluid volume bias by using fundamental magnetic resonance principles and conventional borehole information, they are only effective when the drilling fluid used is water-based mud. The artificial neural networks (ANN) have traditionally been used to correct well log data corrupted by tension pulls on the tool or degraded by poor borehole conditions. They have also been used for cases in which it is not feasible to obtain a traditional set of openhole well log data. In such cases, a neural network is used to generate synthetic openhole data from pulsed neutron logging data. In this application of ANN, the data is first manually interpreted to delineate uncorrupted zones of the data. This interpretation will correspond to zones in which there is no hydrocarbon or the hydrocarbon type is not heavy oil. The aggregate of such zones is used to define a training set. The remaining data is used to define the application data set. Traditional openhole data (such as resistivity, neutron, bulk density, and thorium) is used to train a neural network to predict NMR fluid volumes. The network obtained is then applied to the application data set, and it is shown that the resulting predicted NMR fluid volumes have been corrected for heavy oil effects in the presence of oil-based mud. This paper demonstrates the effectiveness of this approach and discusses the results obtained. It also demonstrates how the process can be automated.

Decision letter: Neural markers of predictive coding under perceptual uncertainty revealed with Hierarchical Frequency Tagging

The clock signal is the heartbeat of modern electronic system, and demands of increasing high‐quality signal has been raised, with the development of science and technology in the field of electronic information. The clock signal with low jitter is helpful to improve the signal‐to‐noise ratio (SNR) of sampled data and obtain high‐precision result. In this article, a programmable clock system of signal generation and distribution is designed for high‐speed acquisition system. The system can provide the clock of low jitter which frequency is as high as 3.2 GHz. The output channels for clock are 14 and each channel for clock can also be configured for synchronizing, delay adjusting or calibrating. Based on the system, a new type of configuration program is developed by using Verilog language. It can directly control the chip which has better compatibility in the application scenario of Field Programmable Gate Array (FPGA) as the master chip. It also provides a reference and guidance for the design and implementation of the dual‐Phase Locked Loop clock chips. When it provides the clocks for the high‐speed acquisition system, the SNR of the acquisition signal has been significantly improved compared with similar acquisition systems. It indicates that the clock system we designed provides an effective way for the high‐speed acquisition system to obtain the data with higher SNR.

Numerous comprehensive well-logging suites have been deployed in recent years to assess unconventional oil and gas reservoirs. These technologies produce extensive and rich data sets that, while powerful, can introduce significant technical challenges during integration into formation evaluation workflows. A key challenge arises when integrating advanced dielectric and spectroscopy logs, each of which yields independent outputs for water saturation and salinity through distinct measurement physics. Present workflows designed to reconcile these and other quantities do not rigorously and consistently incorporate known measurement sensitivities and uncertainties and can vary between log analysts. In unconventional reservoirs, they also rely on user inputs of kerogen maturity and hydrocarbon properties, which can be difficult to ascertain a priori. This underscores the need for a consistent, quantitative interpretation methodology that can precisely integrate advanced logs into a formation evaluation workflow. A novel multiphysics inversion approach has been recently introduced to optimize the various formation property sensitivities of two primary advanced logs: multifrequency dielectric dispersion and geochemical spectroscopy. One of the primary limitations of standalone dielectric interpretation is its insensitivity to salinity estimation at high formation water salinity ranges, which is complemented by chlorine and formation sigma measurements in the multiphysics inversion. Additionally, nuclear magnetic resonance (NMR) porosity, when incorporated into the inversion with the spectral total organic carbon (TOC) measurement, provides unique sensitivities for constraining kerogen and hydrocarbon properties. The multiphysics inversion method quantitatively accounts for these differences in physical sensitivities, as well as their measurement uncertainties, in the solutions for water volume, saturation, formation water salinity, and kerogen and hydrocarbon densities. Combined with advanced mineralogy and porosity evaluations from the logging suites, this methodology produces unified, optimized outputs for advanced well-log interpretation while maximizing the extracted value of the acquired advanced measurement suite. We present case studies as a first exhibition of the new multiphysics inversion technique, integrated into complete petrophysical workflows, for utilizing multifrequency dielectric, geochemical, and NMR logging tools in unconventional reservoir evaluation. We explore reservoirs in the Permian Basin of the United States, including logged intervals over the organic shale pore-fluid systems of the Wolfcamp Formation. We also include an unconventional oil reservoir evaluation in the Vaca Muerta shale of Argentina. Through these studies, we demonstrate how the standardized multiphysics inversion approach supersedes individualized advanced log reconciliation and minimizes uncertainties in their integration. We provide comparisons to standalone interpretations of each advanced log to illustrate the benefits of a simultaneous inversion with discussions of the limitations presented by each approach. Validation of formation water salinity and kerogen properties is performed against available published local data. Finally, we discuss the conditions under which these workflows can be logically extended to conventional reservoir evaluation and carbon capture and storage (CCS) applications, broadening their applicability well beyond unconventional reservoirs.

The main goal of this work was the classification of porosity type for carbonate rocks of aptian section using acoustic and nuclear magnetic resonance (NMR) logs. The adopted methodology (Anselmetti and Eberli.,1999) was applied on three wells which belong to the same basin. Besides the type porosity classification, this methodology also allowed to have a permeability trend curve for the area. The good results obtained on these analyses bring up important information for the area. Moreover, the results assure a very important technical support for well evaluation while the rock data were not yet available, offering a more detailed knowledge of the permoporosity during the well appraisal period.

We discuss an innovative new high-performance apparatus for performing low-field Nuclear Magnetic Resonance (NMR) relaxation times and diffusion measurements on fluids at very high pressures and high temperatures. The apparatus sensor design and electronics specifications allow for dual deployment either in a fluid sampling well logging tool or in a laboratory. The sensor and electronics were designed to function in both environments. This paper discusses the use of the apparatus in a laboratory environment. The operating temperature and pressure limits, and the signal-to-noise ratio (SNR) of the new system exceed by a very wide margin what is currently possible. This major breakthrough was made possible by a revolutionary new sensor design that breaks many of the rules of conventional high pressure NMR sensor design. A metallic sample holder capable of operating at high pressures and temperatures is provided to contain the fluid under study. The sample holder has been successfully tested for operation up to 36 Kpsi. A solenoid coil wound on a slotted titanium frame sits inside the metallic sample holder and serves as an antenna to transmit RF pulses and receive NMR signals. The metal sample holder is sandwiched between a pair of gradient coils which provide a linear field gradient for pulsed field gradient diffusion measurements. The assembly sits in the bore of a low-gradient permanent magnet. The system can operate over a wide frequency range without the need for tuning the antenna to the Larmor frequency. The SNR measured on a water sample at room temperature is more than 15 times greater than that of the commercial low-field system in our laboratory. Thus, the new system provides for data acquisition more than 200 times faster than was previously possible. Laboratory NMR measurements of relaxations times and diffusion coefficients performed at pressures up to 25 Kpsi and at temperatures up to 175 °C with crude oils enlivened with dissolved hydrocarbon gases (referred to as "live oils") are shown. This is the first time low-field NMR measurements have been performed at such high temperatures and pressures on live crude oil samples. We discuss the details of the apparatus design, tuning, calibration, and operation. NMR data acquired at multiple temperatures and pressures on a live oil sample are discussed.

Surface nuclear magnetic resonance (NMR) is a powerful technique providing non-invasive imaging of groundwater. One challenge with the method is that it commonly suffers from low signal to noise ratios (SNR). Two methods to increase SNR are to either develop noise cancellation approaches to reduce the noise level, or to perform the experiment in a manner capable of increasing the signal amplitude. A recent study adopted the latter approach by employing a novel transmit strategy. An adiabatic pulse was employed and observed to produce significant signal improvements compared to the standard transmit method in surface NMR. The advantage of an adiabatic pulse is that it is capable of producing a uniform excitation in the presence of a heterogeneous magnetic field, which describes exactly the transmit conditions in surface NMR. Given the great potential of adiabatic pulses for surface NMR, we explore several factors related to the design of adiabatic pulses intended for application in surface NMR conditions. We investigate how various adiabatic pulses perform in a heterogeneous magnetic field given the limitation that current instrumentation couples the modulation of the current amplitude during the pulse to the instantaneous transmit frequency. That is, only the duration of the adiabatic sweep, the bandwidth through which the pulse sweeps, and frequency modulation throughout the pulse can be directly controlled. A numerical sensitivity analysis of each of these parameters is performed to gain insight into how to design optimal adiabatic pulses for surface NMR. Additionally, a numerically optimized modulation (NOM) approach is implemented to optimize the frequency sweep. The spatial resolution and depth penetration provided by an example adiabatic pulse is also investigated. A trade off between signal amplitude and spatial resolution is observed to be present when employing adiabatic pulses.

Motion estimation in videos is a computationally intensive process. A popular strategy for dealing with such a high processing load is to accelerate algorithms with dedicated hardware such as graphic processor units (GPU), field programmable gate arrays (FPGA), and digital signal processors (DSP). Previous approaches addressed the problem using accelerators together with a general purpose processor, such as acorn RISC machines (ARM). In this work, we present a co-processing architecture using FPGA and DSP. A portable platform for motion estimation based on sparse feature point detection and tracking is developed for real-time embedded systems and smart video sensors applications. A Harris corner detection IP core is designed with a customized fine grain pipeline on a Virtex-4 FPGA. The detected feature points are then tracked using the Lucas---Kanade algorithm in a DSP that acts as a co-processor for the FPGA. The hybrid system offers a throughput of 160 frames per second (fps) for VGA image resolution. We have also tested the benefits of our proposed solution (FPGA + DSP) in comparison with two other traditional architectures and co-processing strategies: hybrid ARM + DSP and DSP only. The proposed FPGA + DSP system offers a speedup of about 20 times and 3 times over ARM + DSP and DSP only configurations, respectively. A comparison of the Harris feature detection algorithm performance between different embedded processors (DSP, ARM, and FPGA) reveals that the DSP offers the best performance when scaling up from QVGA to VGA resolutions.

A method for indirect online over-temperature detection of isolators in high voltage switchgear is proposed，which is based on Na Superionic Conductor CO2 sensor. The over-temperature detecting principle is put forward and an over-temperature detecting system based on the method is introduced. The reliability of over-temperature detection depends on signal noise ratio (SNR), but the sensor and signal processing determine the SNR of CO2 signal. Based on quantitative analysis, it is concluded that Na Superionic Conductor CO2 sensor has higher SNR. By signal processing, the necessity of moving average filter for SNR is demonstrated. A temperature compensation circuit is adopted to eliminate the influence of temperature. In view of the characteristics of rude signal, an appropriate method of real-time digital filtering is used to improve the Signal-to-Noise Ratio. The different features of the signal under over-temperature condition have been analyzed and compared to those under normal conditions. The paper introduces a reliable algorithm for identifying over-temperature detection based on sequential section differences. Experiments prove that the method is feasible.

The radio frequency (RF) excitation pulse of the nuclear magnetic resonance (NMR) logging tool can realize slice measurement by designing shaped pulses. In the case of a certain main magnetic field, the accuracy of the shaped pulse designhas a very important impact on the signal-to-noise ratio (SNR) of the NMR signal and the measurement of the short relaxation signal. Hard pulse excitation will produce an undesirable infinite number of side lobes that may perturb the spins in unwanted regions. Soft pulse can achieve selective excitation and has a better slice profile and shorter energy release time while it is not conducive to the measurement of short relaxation signals. This article focuses on the designof shaped pulses in extreme downhole environments and analyzes the characteristics of the three shaped pulses in the two cases of equivalent bandwidth and equivalent pulse duration. At the same time, a kind of RF-shaped pulse transmitting circuit with phase difference control is realized. According to the pulse type optimization strategy, the appropriate shaped pulse is selected. When echo spacing (TE) >0.6ms, the SNR can be increased to more than 12%. When TE is small, it will automatically switch to the hard pulse mode, which is good for short relaxation measurement.

In nuclear magnetic resonance (NMR) spectroscopy, the signal from nuclear spins is extremely weak, and thus, it has been desired to improve the signal-to-noise ratio (SNR). In this study, we designed a compact low-noise probe and peripheral detection system optimized for low-temperature solid-state NMR spectroscopy. We confirmed the operation of a Tx/Rx switch composed of radio frequency microelectromechanical system (RF MEMS) switches and a preamplifier using high electron mobility transistors (HEMTs), which can operate even at 20 K and under a high magnetic field of ∼10 T. In our compactly designed NMR system, we succeeded in sufficiently reducing both thermal noise and external noise. As a result, the 195Pt- and 27Al-NMR measurements for Pt and Al solid powder samples were performed at 12 T and down to 20 K, showing that the SNR was improved by 40%–50% compared with the conventional system. These experiments also provide information on the low temperature properties and operational characteristics of integrated RF electronic devices, such as the RF MEMS switch, the HEMT, and their combined circuit system.