Hydraulic Data Research Articles

Seepage process understanding at long-existing landslide dams through numerical analysis and hydrological measurements

Landslide dams consist of unconsolidated heterogeneous material and lack engineering measures to drain water and control pore water pressure. They may be porous and seepage through them could potentially lead to piping failure. In this research, the internal processes within a long-existing landslide dam are assessed under transient seepage force. The implemented approach includes a 3D finite element numerical simulation executing fully coupled flow-deformation and consolidation methods based on hydraulic data measurements and geotechnical laboratory tests. The nonlinear constitutive model ‘Hardening Soil’ is applied to accurately calculate the stress-induced pore water pressure, effective stress, deformation, and flow. Further, the possibility of slope failure due to seepage force is investigated through the strength reduction method. The results highlight the dependency of the seepage flow on the corresponding variation of the relative permeability and saturation in the soil mediums under different rates of seepage force. Small rates of seepage force, however, impose deformation at the dam's crown. High effective stress is obtained at negative small rates of seepage force where the long duration of fluctuation is modeled. In the drawdown simulation, there is a reverse relation between effective stress and the rate of the seepage force. Through the modeling process and based on the measured data, two seepage paths are detected within the landslide dam, while their activation depends on the lake level. The modeling approach and the required data analysis are suggested for utilization in further studies regarding the seepage process understanding at the long-existing landslide dams and their hazard assessments in addition to the common geomorphological approaches.

Unsaturated hydraulic property measurements of subtropical anthropogenic (purple) soils in China

AbstractMany anthropogenic soils, often referred to as red bed or purple soils, are distributed in various areas of southern China. Purple soils typically are highly weathered and often lead to natural and engineering hazards because of their relatively poor water retention properties. Knowledge of the unsaturated soil hydraulic properties of purple soils is crucial for their optimal management and various assessment studies. In this work, the hydraulic properties of purple soils from southern China were measured in the laboratory over the full moisture range using a combination of evaporation (HYPROP) and psychrometer (WP4C) approaches. Measured data were analyzed in terms of four different unimodal and bimodal soil hydraulic models. The measurements and analyses showed that bimodality was not overly significant for most samples. The good fit of the Peters–Durner–Iden models furthermore suggested that corner and film flows were important under relative dry conditions. Existing soil pedotransfer functions were found to provide a fairly close match for the slope of water retention curves with the exception of near saturated water contents and the saturated conductivity. To the best of our knowledge, this is the first time that unsaturated hydraulic data of purple soils are provided over the full moisture range.

Hydraulic characterization of Pwales aquifer in Malta Island preparatory for planning managed aquifer recharge (MAR) pilot plant

Whitin the aim to reduce the water demand by increasing water use efficiency and providing alternative water resources, and mainly to meet the demand of good quality irrigation water for agriculture, the Energy and Water Agency of Malta is planning to develop a Managed Aquifer Recharge pilot plant in Pwales Valley to improve the quantitative and qualitative status of the groundwater body. For this reason a detailed hydraulic characterization of the valley was carried out. Specifically, hydraulic properties of the rocks that constitute strata atop of the Pwales aquifer were determined by means of both laboratory measurements on samples and field test carried out in the studied area. The water retention and hydraulic conductivity functions, which relate the matric potential, ψ, and hydraulic conductivity, K, to the water content, θ, respectively, were measured using three experimental methods because each of them allows to obtain data points in a specific wet range. The water retention and hydraulic conductivity functions were measured on samples extracted from blocks of Upper Coralline Limestone formation, that hosts the aquifer, collected in three different quarries: Ghian Tuffieha, Mellieha and San Martin areas. The measured water retention and hydraulic conductivity data were fitted with LABROS SoilView Analysis software that allows to describe the functions and obtain the parameters which are crucial for modelling the water flow and transport processes in the critical zone. In addition, large ring infiltrometer test was carried out to determine the field saturated hydraulic conductivity, Kfs, and the average infiltration rate. Knowledge of the hydraulic characteristics of the Upper Coralline Limestone, completely missing in the scientific literature, allows developing a local groundwater-flow numerical model in order to better describe and understand how the water flows from the soil to the groundwater of the valley and visualize different environmental scenarios such as the potential effects of Managed Aquifer Recharge plant in the Pwales Coastal Groundwater Body.

Efficiency Evaluation of Surface Water Collection Infrastructure during Floods: Information Analysis and Zoning Management

Objective: Flood events, occurring as natural and unexpected phenomena, have become increasingly prevalent in recent decades. Assessing the probability of flood risk and creating flood zoning maps in vulnerable areas, particularly urban regions, are crucial for mitigating flood damages and managing such events. This research aims to determine the flood-prone zones along the Darakeh River, located in Tehran, the capital city of Iran, by integrating the hydraulic model HEC-RAS with Geographic Information System (GIS). Methods: After establishing the physiographic characteristics of the watershed, including area, perimeter, elevation, slope, and time of concentration, influential factors on flood occurrences such as permeability, hydrological soil groups, runoff potential, curve number, land use, and vegetation cover were identified. This information, along with other hydraulic data and flow specifications such as boundary conditions, roughness coefficients, and design discharge, was incorporated into the HEC-RAS model. The model was executed for different return periods of 2, 5, 10, 25, 50, and 100 years. Subsequently, the hydraulic model outputs were transferred to the GIS model to obtain the flood-prone zones along the Darakeh River for the aforementioned return periods. Results: As observed, in the middle section of the Khoshke Channel, which has a lower gradient, the floodplain exhibits a wider extent. Furthermore, along the course of the river and after passing through a stretch of the Khoshke Channel with a reduced gradient, the floodplain's area increases up to the location of the western flood channel. Conclusion: It is concluded that areas with lower river slopes exhibited larger flood-prone zones, highlighting the necessity of increased attention to prevent significant damages in urban areas during flood events.

Induced seismicity data prep: Automate data processing and data set production in Texas and New Mexico using Python and ArcGIS Pro tools

Causal factors of induced seismicity in the Permian Basin are investigated by collecting and processing data on reported earthquakes, hydraulic fracture operations, and salt water disposal (SWD). Data are collected from five online sources: (1) the TexNet Earthquake Catalog, which provides earthquake data for Texas; (2) the TexNet Injection Volume Reporting Tool, which provides daily SWD data for select Texas wells; (3) the FracFocus Chemical Disclosure Registry, which provides hydraulic fracture data to the public; and (4) B3 Insight and (5) IHS Enerdeq Browser, which are proprietary database services that provide current and historical well data through paid subscriptions. TexNet makes their data available to the public at dynamic map websites. We automate data processing and data management using Python and ArcGIS Pro tools. The workflow produces quick, reliable, consistent, and reproducible output. We developed a Python script for each collected data table to filter, select fields, and write a new table. We created ArcGIS Pro Model Builder models for each new table to control format properties at import to the geodatabase. Further models contain customized ArcToolbox tools arranged to run geospatial, quality assurance, and quality control processing steps. In addition to discussing the source data and general workflow, we also review the results of the automated data processing. To illustrate our method, we create areas of investigation around the 5.4 magnitude Coalson earthquake to collect and process available data to create maps, charts, and data products for use in subsequent analysis. We make our Python scripts available on GitHub.

Leveraging deep learning with progressive growing GAN and ensemble smoother with multiple data assimilation for inverse modeling

The incorporation of hard data in geostatistical modeling is crucial for enhancing the accuracy of interpolating or stochastically estimating subsurface spatial features. The hard data at specified points in the model domain serve as a guide in optimizing the unknown parameters to follow the patterns of the hard data. Recently, a novel approach to solving hydrogeologic/reservoir modeling problems has emerged by using deep generative models, specifically generative adversarial networks (GANs), to generate realistic and diverse images of channelized aquifers. This subsequently can be coupled with inverse models to solve parameter estimation problems. This study focused on using an improved GAN, called a progressive growing generative adversarial network (PGGAN), conditioned with hard data to perform parameter estimation of complex facies models by coupling an ensemble smoother with multiple data assimilation (ES-MDA). First, the PGGAN was trained to an image with 128 × 128 resolution. The trained PGGAN was used to generate hydraulic conductivity fields when fed an ensemble of latent variables and hard data. The ES-MDA then was used to update the latent variable with the help of hydraulic head data obtained from the groundwater model. The approach was tested on synthetic hydraulic conductivity data. Results show that this approach was able to perform efficient estimation of an unknown facies model domain. Additionally, the proposed method was applied to a different test case of a facies model exhibiting different statistical characteristics. The results were satisfactory, demonstrating that the method is not constrained to the particular hydraulic conductivity fields introduced in the generator’s training.

Application of real-time water temperature prediction system in winter for long-distance water diversion projects

ABSTRACT Water diversion projects in high-latitude areas often reduce the risk of ice jams in winter by reducing the water transfer flow, which might cause the waste of water transfer benefits. This paper establishes a real-time prediction system of water temperature in winter, which can predict the change in water temperature by inputting the air temperature forecast data and the current hydraulic data. Taking the middle route of the south-to-north water diversion project as the background, the model parameters calibration and system application testing at different time periods are carried out. The results show that the prediction errors of water temperature for the 1 and 7 days are relatively small, and the prediction errors of water temperature at four observation stations can be controlled within ±0.3 and ±0.6 °C, with the root mean square error (RMSE) ranging from 0.07 to 0.25 and 0.12 to 0.36, respectively. The 15-day water temperature prediction results are greatly affected by air temperature input conditions. The prediction errors for the first 7 days are relatively small, ranging from −0.59 to 0.36 °C, and the errors for the last 8 days increase as the accuracy of the air temperature forecast decreases, ranging from −2.42 to 0.22 °C.

Modeling hydraulic conductivity function of frozen soil

Hydraulic conductivity function for frozen soils (HCFF) is crucial for accurately simulating the water transfer process in cold regions, impacting hydrological states and frost heave diseases. However, the HCFF model capturing the relation between hydraulic conductivity and unfrozen water content (θ) or temperature (T) remains an open question. This study delves into HCFF encompassing both empirical and prediction models. This study begins with introducing θ-form and innovative T-form empirical HCFF models, showcasing their solid performance in fitting measured hydraulic conductivity data. Furthermore, the extension of the T-form empirical model to incorporate vapor flow and bimodal soils, through the introduction of maximum vapor conductivity and weighting factors, demonstrates a closer alignment with physical mechanisms and observed trends. In terms of predicting HCFF, the study provides a theoretical foundation through statistical hydraulic conductivity models and thermodynamic equilibrium in frozen soils. Additionally, it clarifies three routes for HCFF prediction—single Soil Freeze Characteristic Curve (SFCC), single Soil Water Characteristic Curve (SWCC), and a combination of the two—and validates their effectiveness through test results and comparison of HCFF prediction outcomes using SWCC and SFCC data from specific soil samples. In practice, the empirical and prediction models are recommended for available hydraulic conductivity data and available SFCC or SWCC data, respectively. Overall, this study not only lays the theoretical groundwork for most prediction models but also presents a valid empirical equation for modeling hydraulic conductivity function tailored specifically for frozen soils.

Fault diagnosis study of hydraulic pump based on improved symplectic geometry reconstruction data enhancement method

Aiming at the problem of poor consistency between the enhanced samples and the original samples in the current data enhancement methods. In this paper, we propose a data enhancement method with improved symplectic geometry reconstruction. The method expands a sufficient number of augmented samples while ensuring high similarity to real fault samples. First, the components to be augmented and their weights are selected from the decomposed symplectic geometric modal components using a random sampling method. Second, a data-weighted scaling criterion is formulated to augment the modal component amplitudes. Then, the summation and reconstruction of all amplitude enhancement components are performed on the basis of ensuring that the statistical characteristics of the enhanced samples remain consistent with the original samples. Finally, the proposed method is combined with the deep residual network, and the experimental dataset of hydraulic pump failure simulation is utilized to verify the effectiveness of the proposed method in obtaining the enhanced fault sample information. The diagnostic results show that the fault diagnosis model established by the method has higher accuracy and convergence ability in hydraulic pump data imbalance fault diagnosis compared with other comparative models.

Using major ions and stable isotopes to improve conceptualisation of a spring-aquifer system in the Galilee Basin, Australia

Developing conceptual models is a critical step in hydrogeological studies that should utilise multiple lines of evidence and data types to minimise conceptual uncertainty, particularly in data-sparse systems. This study used new and existing major ion and isotope (O, H, Sr, C) data sets to refine a previous hydraulic-head-based conceptual model of the Galilee Basin (Australia). The analyses provide evidence for the locations of recharge and discharge areas and determine hydrochemical processes along flow paths to improve understanding of potential source waters to the Doongmabulla Springs Complex (DSC) and to infer mixing within, or exchange between aquifer units. There was good agreement between previously inferred recharge and discharge areas defined using hydraulic head data and interpretations from hydrochemical evolution along groundwater flow pathways, at least where data were available. Major ion and isotope data suggest that the DSC likely receives water from both a relatively shallow, local flow path and a deeper regional flow path. This observation is relevant to previous concerns about threats to the DSC, as mine-induced drawdown may impact the relative contributions to spring discharge from different recharge sources and aquifers. Silicate weathering in the deeper Clematis Formation and Dunda Beds, and evapotranspiration in the overlying Moolayember Formation have strong control on the total dissolved solids content. These findings suggest that the Clematis Formation and Dunda Beds are hydrochemically distinct from the Moolayember Formation, with limited exchange between these aquifers, which has important implications for model conceptualisation and ongoing monitoring of mining activities in the Galilee Basin.

As Hype Fades, LLMs Gaining Acceptance in Upstream as New Age Research and Coding Tool

After using ChatGPT-4 for more than a year, Clinton Lott has made familiarity with the large language model (LLM), a requirement for new software programming hires. The director of Houston-based Spotter Tech gave this reasoning: “Taking 2 hours to do something that ChatGPT can do in 15 seconds is unacceptable today.” It’s a line you’re likely to hear from the most ardent champions of LLMs which have simultaneously reignited the world’s interest in and ire for artificial intelligence (AI). And while Spotter Tech, a small hydraulic fracturing data specialist, may be an exception with its hiring practices, it is far from the only upstream company embracing the new technology. Both BP and Shell have recently given thousands of employees access to Microsoft’s new cloud service called Copilot, which at its core is powered by GPT-4, OpenAI’s latest and most capable LLM. Others including ExxonMobil have placed use of LLMs under lockdown while still signaling a long-term interest in their embrace. As chair of SPE’s Data Science & Engineering Analytics Technical Section, Pushpesh Sharma has spent the past several months speaking with peers in the industry about how and where LLMs are being used. The product manager for field automation firm Aspen Technology told JPT that it’s clear from those conversations that a good number of energy firms are interested in tailoring the technology to their needs, albeit many are doing so quietly as it remains early days. “The big focus right now is on what they call fine tuning,” he said. “You take an existing base model that one of the big tech companies have created and make it more of a fit for your business. It’s kind of like taking a graduate student and then letting them work on their PhD with very specific subject matter.” While OpenAI’s ChatGPT has become the popular symbol of LLMs, there are a growing number of alternatives in the mix. In February, Google introduced its latest model called Gemini 1.5 which seeks to rival GPT-4. There are also open source LLMs that oil and gas companies can use as templates for internal projects. Or they may turn to other big tech firms such as NVIDIA and Amazon Web Services (AWS) which are in the business of helping industrials build and train their own private, custom LLMs. SPE joined the trend after inking a Memorandum of Understanding (MoU) in October with AI firm i2k Connect and Aramco Americas. The collaboration is expected to result in a new domain-specific LLM that industry engineers and researchers can use to answer questions with more technical depth than can be done with the current slate of commercial offerings. One enabling aspect of this current phase of experimentation with LLM technology is retrieval-augmented generation (RAG). The method is considered essential for models to provide more-accurate and actionable responses by extending their scope beyond initial training datasets to include more authoritative and even real-time data.

Stress‐induced anisotropy in Gulf of Mexico sandstones and the prediction of in situ stress

AbstractThe strong sensitivity of velocity to stress observed in many sandstones originates from the response of stress‐sensitive discontinuities such as grain contacts and microcracks to a change in effective stress. If the change in stress is anisotropic, then the change in elastic wave velocities will also be anisotropic. Characterization of stress‐induced elastic anisotropy in sandstones may enable estimation of the in situ three dimensional stress tensor with important application in solving problems occurring during drilling, such as borehole instability, and during production, such as sanding and reservoir compaction. Other applications include designing hydraulic fracture stimulations and quantifying production‐induced stresses which may lead to rock failure. Current methods for estimating stress anisotropy from acoustic anisotropy rely on third‐order elasticity, which ignores rock microstructure and gives elastic moduli that vary linearly with strain. Elastic stiffnesses in sandstones vary non‐linearly with stress. Using P‐ and S‐wave velocities measured on Gulf of Mexico sandstones, this non‐linearity is found to be consistent with a micromechanical model in which the discontinuities are represented by stress‐dependent normal and shear compliances. Stress‐induced anisotropy increases with increasing stress anisotropy at small stress but then decreases at larger stresses as the discontinuities close and their compliance decreases. When the ratio of normal‐to‐shear compliance of the discontinuities is unity, the stress‐induced anisotropy is elliptical, but for values different from unity, the stress‐induced anisotropy becomes anelliptic. Although vertical stress can be obtained by integrating the formation's bulk density from the surface to the depth of interest, and minimum horizontal stress can be estimated using leak‐off tests or hydraulic fracture data, maximum horizontal stress is more difficult to estimate. Maximum horizontal stress is overpredicted based on third‐order elasticity using measured shear moduli, with estimates of pore pressure, vertical stress and minimum horizontal stress as input. The non‐linear response of grain contacts and microcracks to stress must be considered to improve such estimates.

Swimming behaviour of Atlantic salmon kelts migrating past a hydropower plant dam: Effects of hydraulics and dam operations

Hydropower plants commonly impede the downstream migration of Atlantic salmon (Salmo salar) kelts. Thus, understanding the effects of hydraulic conditions on kelt behaviour and passage performance at dams is crucial for developing effective mitigation measures. In this study, we investigated the influence of hydraulic conditions on kelt passage performance and swimming behaviour at a Norwegian hydropower plant. We combined biological data from 48 kelts collected via acoustic telemetry with hydraulic data modelled using computational fluid dynamics. We assessed kelt passage performance using metrics such as time-to-pass, total number of detections, and total number of detections per day. Additionally, we analysed swimming depths and speeds in relation to the hydraulic conditions created by different dam operating conditions. We found that the dam operation schedule impacted the kelts' ability to find a route past the dam. Though kelts could have passed the dam throughout the study period via a submerged pipe at the dam (which had seemingly sufficient discharge for the kelts to find), 98 % of the kelts instead waited for a spill gate to open partway through the study period. The swimming depth analysis indicated diel variation, with kelts swimming nearer to the water surface during the night. We found that swimming speed increased with increasing kelt body length, particularly under high turbulence kinetic energy and during the day. Furthermore, kelts swam faster as water velocity increased, but slowed down again as turbulence intensity increased. Our findings reveal the effects of hydraulic conditions and dam operations on the migration behaviour of Atlantic salmon kelts. This provides valuable insights for developing strategies to optimise dam operations and improve fish passage performance, including the need to spill enough water to increase passage success and will contribute to sustainable management of Atlantic salmon populations in regulated rivers.

Coastal flowing artesian wells and submarine groundwater discharge driven by tidal variation at TaiCOAST site in Taoyuan, Taiwan

Study regionThis study was conducted at the TaiCOAST site, Taoyuan City, Taiwan. Study focusThree coastal flowing artesian wells (FAWs) were observed at the TaiCOAST site that do not match the well-known driving mechanisms of geological or topographical control. The observed phenomenon has not been discussed in the literature. This study collected hydraulic head data for the three coastal FAWs and one onshore well at the TaiCOAST site. A multi-phase (freshwater and seawater) model of groundwater in porous media was constructed to investigate the mechanism of the coastal FAWs and quantify submarine groundwater discharge (SGD). New hydrological insights for the regionThe rare observations of coastal FAWs at the TaiCOAST site were found to be neither geologically, topographically, nor recharge controlled. The constructed numerical model indicates that the coastal FAWs are driven by an upward flow due to the sharp interface between freshwater and seawater, which acts as a barrier. The depth profile of Darcy’s velocity was obtained from the calibrated model. The average Darcy’s velocity of SGD is (2.96 ± 0.61) × 10−6 m/s, which is similar to value reported in the literature. This study provides new insight into the mechanism of FAWs in coastal areas. The results can serve as an important reference for investigations of SGD.

Study of water channels and gates to improve water wheel performance

Glintung Water Street (GWS) in Malang City harnesses water flow in its canal to drive water wheels for electricity generation. However, the discharge of water has not been optimized, necessitating the design of the canal and sluice gate to maximize discharge while mitigating flooding risks. This research aims to determine the optimal design and operational system of the sluice gate to minimize backwater impact. Data collection involved observational techniques and literature review, including hydrology, map, and hydraulic data. Results indicated that the required discharge for the micro-hydro power plant (PLTMH) in the Glintung Water Street (GWS) area was 88 liters or approximately 0.088m3/s. The primary channel's flow rate was measured at 0.104 m3/s, with planned channel inflow at 0.088 m3/s. The sluice gate effectively prevented backwater impact, as water levels did not surpass the sluice height. Planning involved a sluice gate made of mild steel plate (100 cm x 100 cm) with a door height of 200 cm and a thickness of 0.18 cm, operated by a handlebar with a diameter of 2 cm. In conclusion, the backwater effect on the primary channel's water level was minimal, affirming the efficacy of the designed sluice gate.

A Computational Tool to Track Sewage Flow Discharge into Rivers Based on Coupled HEC-RAS and DREAM

Worldwide abatement of untreated sewage discharge into surface water is a challenging task. Sewage discharging into surface waters has a detrimental impact on water quality. This paper presents a MATLAB (R2018b) framework designed to identify sewage flow discharges into rivers from an inverse problem-solving perspective. The computational tool integrates a hydrodynamic model using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 5.0.0) and an open-source toolbox for Differential Evolution Adaptive Metropolis (DREAM) as the inverse problem method. The proposed framework can effectively infer discharge sources in scenarios of highly transient flow based on hydraulic data at pre-set monitoring sites. To validate its capabilities, one hypothetical case and two real cases of sewage flow discharges entering a river were used to test the developed modeling framework. The results based on three performance metrics showed that this mathematical tool can be extended to simulate complex hydrodynamic flow patterns. This accomplishment underscores its potential as a valuable asset for environmental monitoring and water quality restoration efforts.

Prediction of absolute unsaturated hydraulic conductivity – comparison of four different capillary bundle models

Abstract. To model water, solute, and energy transport in porous media, it is essential to have accurate information about the soil hydraulic properties (SHPs), i.e., the water retention curve (WRC) and the soil hydraulic conductivity curve (HCC). It is important to have reliable data to parameterize these models, but equally critical is the selection of appropriate SHP models. While various expressions for the WRC are frequently compared, the capillary conductivity model proposed by Mualem (1976a) is widely used but rarely compared to alternatives. The objective of this study was to compare four different capillary bundle models in terms of their ability to accurately predict the HCC without scaling the conductivity function by a measured conductivity value. The four capillary bundle models include two simple models proposed by Burdine (1953) and Alexander and Skaggs (1986), which assume a bundle of parallel capillaries with tortuous flow paths, and two more sophisticated models based on statistical cut-and-random-rejoin approaches, namely those proposed by Childs and Collis-George (1950) and the aforementioned model of Mualem (1976a). To examine how the choice of the WRC parameterization affects the adequacy of different capillary bundle models, we utilized four different capillary saturation models in combination with each of the conductivity prediction models, resulting in 16 SHP model schemes. All schemes were calibrated using 12 carefully selected data sets that provided water retention and hydraulic conductivity data over a wide saturation range. Subsequently, the calibrated models were tested and rated by their ability to predict the hydraulic conductivity of 23 independent data sets of soils with varying textures. The statistical cut-and-random-rejoin models, particularly the Mualem (1976a) model, outperformed the simpler capillary bundle models in terms of predictive accuracy. This was independent of the specific WRC model used. Our findings suggest that the widespread use of the Mualem model is justified.

Increases in trade secret designations in hydraulic fracturing fluids and their potential implications for environmental health and water quality

Hydraulic fracturing is an increasingly common method of oil and gas extraction across the United States. Many of the chemicals used in hydraulic fracturing processes have been proven detrimental to human and environmental health. While disclosure frameworks have advanced significantly in the last 20 years, the practice of withholding chemical identities as “trade secrets” or “proprietary claims” continues to represent a major absence in the data available on hydraulic fracturing. Here, we analyze rates of trade secret claims using FracFocus, a nationwide database of hydraulic fracturing data, from January 1, 2014 to December 31, 2022. We use the open-source tool Open-FF, which collates FracFocus data, makes it accessible for systematic analysis, and performs several quality-control measures. We found that the use by mass of chemicals designated as trade secrets has increased over the study time period, from 728 million pounds in 2014 to 2.96 billion pounds in 2022 (or a 43.7% average yearly increase). A total of 10.4 billion pounds of chemicals were withheld as trade secrets in this time period. The water volume used (and therefore total mass of fracturing fluid) per fracturing job has shown a large increase from 2014 to 2022, which partly explains the increase in mass of chemicals withheld as trade secrets over this time period, even as total fracturing jobs and individual counts of proprietary records have decreased. Our analysis also shows increasing rates of claiming proppants (which can include small grains of sand, ceramic, or other mineral substances used to prop open fractures) as proprietary. However, the mean and median masses of non-proppant constituents designated as trade secrets have also increased over the study period. We also find that the total proportion of all disclosures including proprietary designations has increased by 1.1% per year, from 79.3% in 2014 to 87.5% in 2022. In addition, most disclosures designate more than one chemical record as proprietary: trade secret withholding is most likely to apply to 10–25% of all records in an individual disclosure. We also show the top ten reported purposes that most commonly include proprietary designations, after removing vague or multiple entries, the first three of which are corrosion inhibitors, friction reducers, and surfactants. Finally, we report the top ten operators and suppliers using and supplying proprietary chemicals, ranked by mass used or supplied, over our study period. These results suggest the importance of revisiting the role of proprietary designations within state and federal disclosure mechanisms.

Tensile Fractures and in situ Stress Measurement Data Constraints on Cretaceous–Present Tectonic Stress Field Evolution of the Tanlu Fault Zone in Shandong Province, North China Craton

AbstractTectonic stress fields are the key drivers of tectonic events and the evolution of regional structures. The tectonic stress field evolution of the Tanlu fault zone in Shandong Province, located in the east of the North China Craton (NCC), may have preserved records of the NCC's tectonic history. Borehole television survey and hydraulic fracturing were conducted to analyze the paleo and present tectonic stress fields. Three groups of tensile fractures were identified via borehole television, their azimuths being NNW–SSE, NW–SE and NE–SW, representing multiple stages of tectonic events. Hydraulic fracturing data indicates that the study region is experiencing NEE–SWW‐oriented compression and nearly‐N–S‐oriented extension, in accordance with strike‐slip and compression. Since the Cretaceous, the orientation of the extensional stress has evolved counterclockwise and sequentially from nearly‐NW–SE‐oriented to NE–SW‐oriented and even nearly N–S‐oriented, the stress state having transitioned from strike‐slip‐extension to strike‐slip‐compression, in association with the rotating and oblique subduction of the Pacific Plate beneath the NCC, with the participation of the Indian Plate.

A simplified approach to hydraulic model calibration using HEC-RAS for water quality simulation Ciujung river

One approach to identifying the pollution load released by the industry quickly and accurately is to create a model taken from a reliable database. HEC-RAS was used in generating the Ciujung River water quality database to be used in simulating various scenarios in modeling water quality projections. In fact, the measurement data sometimes gives a misleading interpretation for understanding the relationship between hydraulics and water quality. Therefore, a simple approach to calibrating the hydraulic data is then developed. The effort to achieve mass conservation philosophy is taken into account in this research, such as bed elevation correlation, boundary condition adjustment downstream and upstream, Manning correction, and justification of flow events according to the limitations of measurement data. The result shows that the proposed simple approach in this research gives a value of coefficient correlation 0,9, at which mass conservation is achieved.