Estimation Of Hydraulic Properties Research Articles

Surrogate estimation of soil water retention characteristics of seasonally impounded soils

Measurement of soil hydraulic properties is an arduous task, especially to meet the large scale data requirements. This has prompted numerous studies on surrogate estimation of hydraulic properties. Objective of this study was to build regional hydraulic database and develop pedotransfer functions (PTF) for estimation of water retention characteristics (WRC) of seasonally impounded shrink-swell soils. Soil samples from 41 soil profiles were analyzed in the laboratory for soil-water retention at nine pre-determined suction pressure points and basic soil properties. These data were used for calibrating neural PTFs to predict Van Genuchten (VG) function parameters for prediction of WRC from basic soil properties data. Routine method of deriving and relating retention function parameters to basic soil properties and Neuro-m method resulted in unacceptable PTFs (RMSE > 0.05 m3 m-3) . In place of parameters derived from SWRC, we used ‘Rosetta’ estimates of VG parameters to relate basic soil properties. Resultant hierarchical parametric PTFs with prediction accuracy of RMSE

Soil Hydraulic Property Estimation Using Remote Sensing: A Review

A review of recent developments related to soil hydraulic property estimation using remote sensing is presented. Several soil hydraulic parameter estimation techniques using proximal‐, air‐, or satellite‐based remotely sensed soil moisture, land surface temperature, and/or evapotranspiration time series have evolved over the past decades. In particular, microwave remote sensing of near‐surface soil moisture has played a key role in this respect. Inverse modeling, regression techniques, data assimilation methods by utilizing soil–vegetation–atmosphere transfer models, genetic algorithm based optimization, and uncertainty quantification using ensemble based approaches under synthetic and field conditions have been developed and adapted over these years. In this context multiple approaches for estimating effective soil hydraulic parameters at different spatial scales (e.g., point to remote sensing footprints) by various scaling methods are also summarized. Methods reviewed include traditional bottom‐up approaches, such as homogenization of local scale properties up to footprint scale, as well as newer top‐down approaches, such as use of inverse modeling of remotely sensed soil moisture or temperature using genetic algorithms, Markov Chain Monte Carlo simulations with data assimilation, and multi‐scale Bayesian neural networks. While new remote sensing platforms provide powerful tools and techniques for noninvasive soil hydraulic property estimation of the vadose zone at multiple scales, current limitations including vision for future developments are also summarized.

Parameter Variability and Drought Models: A Study Using the Agricultural Reference Index for Drought (ARID)

Water stress is one of the crucial factors that determine crop yield losses. The Agricultural Reference Index for Drought (ARID) is a generic plant water stress index that estimates the level of water stress as the ratio of actual evapotranspiration to reference evapotranspiration, on a daily time step, considering a hypothetical grass reference crop. The objective of this study was to determine the effect of soil inter‐ and intraclass variability, plant characteristics, and climate locations on ARID output using global sensitivity and uncertainty analysis. Root zone depth, maximum uptake factor, the wilting point (θwp), field capacity (θfc), drainage coefficient, and runoff curve number were the ARID model parameters, while inputs consisted of rainfall and reference evapotranspiration. Global sensitivity analysis was performed for five locations in the southeastern United States and four soil types, sandy loam, silt loam, sandy clay loam, and silty clay, using three methods, the method of Sobol’, partial correlation coefficients, and partial rank correlation coefficients. Sensitivity analyses results indicated the monotonic and linear nature of ARID. The effect of location on the results of this study was minimal. Root zone depth was found to be the most influential model parameter, followed by soil hydraulic properties (i.e., θfc and θwp). Uncertainty in ARID outputs varied from soil to soil (intersoil variability), with soils having similar sand content showing similar results. Considerable uncertainty was seen in the sandier soils, indicating the need for accurate estimation of soil hydraulic properties in such soils.

Estimating Effective Soil Hydraulic Properties Using Spatially Distributed Soil Moisture and Evapotranspiration

With the development of many earth‐observing remote sensing (RS) platforms, spatially distributed remote sensing products are becoming critical inputs to many hydrologic and meteorological models. Remotely sensed soil moisture (SM) and evapotranspiration (ET) including ground‐based data have the potential to be used for estimating pixel‐scale soil hydraulic parameters. However, only a few studies have been conducted to better understand the impact of assimilating both SM and ET in estimating soil hydraulic properties of the root zone. In this study, we used inverse modeling based on the Noisy Monte Carlo Genetic Algorithm by linking RS SM and ET derived from the Surface Energy Balance Algorithm for Land for estimating pixel‐scale effective soil hydraulic properties. Walnut Creek (Iowa), Brown (Illinois), and Lubbock (Texas) test sites were selected to assess the performance of this approach from point to satellite scales using synthetic and validation experiments. For comparison purposes, inverse modeling results were analyzed under three scenarios (ET only, SM only, and SM + ET in the optimization criteria). These results showed that considering both SM and ET components improved the estimations of effective soil hydraulic properties and reduced their uncertainties better than SM or ET only. Overall, although uncertainty exists, our proposed SM + ET based scheme performed well in estimating effective soil hydraulic properties at multiple spatial scales (point, airborne, and satellite footprints) under various hydroclimatic conditions.

Remote Sensing for Vadose Zone Hydrology—A Synthesis from the Vantage Point

Ground‐based, air‐borne, and space‐borne remote sensing techniques have evolved over the past several decades and provided many new techniques for estimating various land surface attributes at multiple scales related to mass and energy dynamics. The vadose zone, encompassing land surface, root zone, and the deeper soil profile down to the groundwater table, is a complex domain, particularly related to various hydrologic and biological processes across different scales. In this zone, spatial distributions and temporal dynamics of soil moisture and evapotranspiration (ET), including their interdependence, are critical to climate feedback, hydrology, and plant canopy health. Their temporal and spatial variability across catchments affect surface and subsurface runoff, modulates evaporation and transpiration, determines the extent of groundwater recharge and contaminant transport, and initiates or sustains feedback between the land surface and the atmosphere. With the recent development and deployment of various remote sensing platforms working with different techniques such as optical, microwave, gravitational, infrared, and other sensors, improved temporal and spatial measurement or estimates of soil moisture, evapotranspiration, soil hydraulic parameters, soil salinity, and vegetation attributes are possible. In this special section “Remote Sensing for Vadose Zone Hydrology,” 14 contributions on fundamental and applied studies using different remote sensing platforms, including satellite retrieval algorithm development, data assimilation techniques, scaling issues, ground validation, and field applications in the context of vadose zone hydrology are presented. The foci of these papers range across root zone soil moisture retrieval and variability, evapotranspiration dynamics and distribution, agricultural water management, soil hydraulic and mechanical property estimation, ecosystems assessment, land–atmosphere interaction, and land surface hazard assessment. Here we organized the summary of these papers in four overlapping sections including (i) estimation, variability, scaling, and data assimilation of soil moisture by microwave remote sensing; (ii) estimating evapotranspiration by remote sensing and water management applications; (iii) estimating vadose zone properties by remote sensing; and (iv) ground‐based soil moisture for calibration and validation of microwave remote sensing.

Estimate of Soil Hydraulic Properties from Disc Infiltrometer Three-dimensional Infiltration Curve: Theoretical Analysis and Field Applicability

This paper describes a new method (NSQE) to estimate soil hydraulic properties (sorptivity, S, and hydraulic conductivity, K) from full-time cumulative infiltration curves. The technique relies on an inverse procedure involving the quasi-exact equation of Haverkamp et al. (1994). The numerical resolution is described and the sensitivity of the method is theoretically evaluated, showing that the accuracy of the estimates depends on the measured infiltration time. A new procedure to detect and remove the effect of the contact sand layer on the cumulative infiltration curve is also given. The method was subsequently compared to the differentiated linearization procedure (DL), which calculate K and S from the simplified Haverkamp et al. (1994) equation, valid only for short to medium times. A total of 264 infiltration measurements performed with a 10cm diameter disc under different soil conditions were used. Compared to the DL procedure, field measurements showed that the NSQE method allowed better estimates of soil hydraulic properties, independently on the infiltration noise and the presence of contact sand layer. Overall, although comparable S values were estimated with both methods, the longer infiltration times allowed by the proposed method made this procedure more accurate estimations of K. In conclusion, the NSQE method have shown to be a significant advance to accurate estimate of the soil hydraulic properties form the transient water flow.

Assessment of the SiSPAT SVAT Model for Irrigation Estimation in South-East France

In this study, we assess the interest of using a Soil – Vegetation – Atmosphere – Transfer model, the SiSPAT (Simple Soil Vegetation Atmosphere Transfer) model, which solves the surface energy balance, for the evaluation of theoretical crop water requirements in south-east France. First the relevance of the model results, when parameterized using information extracted from a soil data base and pedotransfer functions for the estimation of soil hydraulic properties, and when vegetation characteristics are prescribed using available data bases is assessed. We use long term time series of soil water content profiles for this purpose. The results show that evapotranspiration, as simulated by SiSPAT is sensitive to the soil parameter specification leading to large uncertainties in the model results.Then, we present two methods implemented in SiSPAT to compute irrigation requirements. The first option mimics the soil water balance model principles by estimating the irrigation from the available soil water capacity filling. The second option relies on the model physics and estimates the difference between actual transpiration and the value corresponding to a minimal stomatal resistance, i.e. without water stress. Aspersion and drip irrigation can be simulated. Nine crop are chosen for the model evaluation. A comparison with two other water balance models is performed. The three models are consistent with determination coefficient between the simulated annual irrigation generally larger than 0.4. However, differences of the interannual irrigation needs, larger than several 100mm, are sometimes found, especially for drip irrigation. This work provides a quantification of expected uncertainties when using water balance models or physically-based models for irrigation needs estimation.

Simultaneous Estimation of Soil Hydraulic and Root Distribution Parameters from Lysimeter Data by Inverse Modeling

Weighable lysimeters are powerful measurement systems for identifying soil hydraulic processes and properties, because the boundary fluxes (precipitation, actual evapotranspiration, and seepage across the bottom) can be determined very precisely. However, root water uptake by plants and the soil water flux are interrelated. Thus, the simultaneous estimation of root water uptake parameters and soil hydraulic parameters from macroscopic state observations is a challenge. In this study we investigated the possibility of simultaneously estimating root water uptake and soil hydraulic parameters by inverse simulation of soil water flow in monolithic lysimeters under atmospheric boundary conditions. We used the Richards equation and a macroscopic root water uptake model to simulate the processes. The amount of information needed for the unique identification of parameters was analyzed and the magnitude of their uncertainties was investigated. To check the principal feasibility of our approach, we first examined synthetic data sets for different scenarios and instrumentation campaigns that differed in their information content and complexity of soil properties. The investigations of synthetic data showed that for homogeneous profiles, cumulative outflow and profile-averaged water content data contained enough system information to allow the simultaneous estimation of soil hydraulic properties and root-distribution parameters. In contrast, for soil profiles consisting of two layers, unique soil hydraulic parameters and the correct rooting depth could only be estimated if matric potential measurements from both layers were included in the objective function. To test the procedure with real data, soil hydraulic properties of the grass-reference lysimeter at Wagna (Austria) were estimated using actual measurements. Water dynamics in the lysimeter could be described well by an effective parameterization assuming a homogeneous soil profile. Furthermore, the system behavior under different boundary conditions could be predicted adequately with the estimated parameters.

Inverse Estimation of Soil Hydraulic and Root Distribution Parameters from Lysimeter Data

We investigated the feasibility of simultaneous identification of soil hydraulic and root‐distribution parameters by inverse simulation of soil water flow in monolithic lysimeters under atmospheric boundary conditions using the Richards equation and a macroscopic root water uptake model. Weighable lysimeters are powerful test systems for this purpose because the boundary fluxes (precipitation, actual evapotranspiration, and seepage across the bottom) can be determined very precisely. We analyzed the amount of information needed for the unique identification of parameters and investigated the magnitude of their uncertainties. First, we examined synthetic data sets for different scenarios and instrumentation campaigns that differed in their information content and complexity of soil properties. Atmospheric boundary conditions as measured at the lysimeter station in Wagna, Austria, were used as forcing data. The results show that for homogeneous profiles, cumulative outflow and profile‐averaged water content data contain enough system information to allow the simultaneous estimation of soil hydraulic properties and root‐distribution parameters. In contrast, for soil profiles consisting of two layers, unique soil hydraulic parameters and the correct rooting depth could only be estimated if matric potential measurements from both layers were included in the objective function. Finally, soil hydraulic properties of the grass‐reference lysimeter in Wagna were estimated using real measurements. Water dynamics in the lysimeter could be described well by an effective parameterization assuming a homogeneous soil profile. Furthermore, the system behavior under different boundary conditions could be predicted adequately with the estimated parameters. This demonstrates the usefulness of the identified system properties for predictive modeling.

Microflowmeter–tension disc infiltrometer: Part II – Hydraulic properties estimation from transient infiltration rate analysis

Summary Measurements of soil sorptivity ( S 0 ) and hydraulic conductivity ( K 0 ) are of paramount importance for many soil-related studies involving disciplines such as agriculture, forestry and hydrology. In the last two decades, the disc infiltrometer has become a very popular instrument for estimations of soil hydraulic properties. The previous paper in this series presented a new design of disc infiltrometer that directly estimates the transient flow of infiltration rate curves. The objective of this paper is to present a simple procedure for estimating K 0 and S 0 from the linearisation of the transient infiltration rate curve with respect to the inverse of the square root of time (IRC). The technique was tested in the laboratory on 1D sand columns and 1D and 3D 2-mm sieved loam soil columns and validated under field conditions on three different soil surfaces. The estimated K 0 and S 0 were subsequently compared to the corresponding values calculated with the Vandervaere et al. (2000) technique, which calculates the soil hydraulic parameters from the linearisation of the differential cumulative infiltration curve with respect to the square root of time (DCI). The results showed that the IRC method, with more significant linearised models and higher values of the coefficient of determination, allows more accurate estimation of K 0 and S 0 than the DCI technique. Field experiments demonstrate that the IRC procedure also makes it possible to detect and eliminate the effect of the sand contact layer commonly used in the disc infiltrometry technique. Comparison between the measured and the modelled cumulative infiltration curves for the K 0 and S 0 values estimated by the DCI and IRC methods in all the 1D and 3D laboratory experiments and field measurements shows that the IRC technique allowed better fittings between measured and modelled cumulative infiltration curves, which indicates better estimations of the soil hydraulic properties.

Coupled hydrogeophysical inversion of electrical resistances and inflow measurements for topsoil hydraulic properties under constant head infiltration

ABSTRACTAccurate estimation of topsoil hydraulic properties is important for understanding water flow and solute transport in the vadose zone. Coupled hydrogeophysical inversion schemes that enable the use of multiple geophysical and hydrological data for the estimation of soil hydraulic properties have recently been proposed. In these coupled inversion schemes, a hydrological model describing the process under investigation is coupled to a forward geophysical model and hydraulic parameters are directly estimated from geophysical measurements. While these schemes provide a suitable platform for the integration of multiple geophysical and hydrological data, efficient methods to combine these data types for improved parameter estimation still warrant investigation. In this study, we investigated the feasibility of estimating three topsoil Mualem‐van Genuchten parameters from the fusion of inflow and electrical resistance measurements obtained under constant head infiltration. In addition to using only inflow or electrical resistances, we investigated three methods of combining these data for improved estimation of topsoil hydraulic parameters. Our results show that using inflow alone does not provide a unique solution to the inverse problem. Better results are obtained with the additional use of electrical resistance data. We show that successful data fusion within the coupled hydrogeophysical inversion framework depends on the choice of an appropriate objective function. We obtained the best data fusion results with an objective function defined as the sum of the root mean square error of both data types normalized by the standard deviation of the respective measurements. In this case, the inverted hydraulic parameters were very comparable to reference values obtained from a multi‐step outflow experiment carried out with undisturbed soil cores from the experimental site. It is concluded that the coupled hydrogeophysical inversion frame‐work is a promising tool for non‐invasive near‐surface hydrological investigations.

Microseismic estimates of hydraulic diffusivity in case of non-linear fluid-rock interaction

SUMMARY As a rule, rock stimulation by fluid injection induces microearthquakes. Assuming that pore-fluid pressure diffusion is responsible for this phenomenon, one can use seismicity to estimate the hydraulic properties of rocks. Previously, for the estimation of hydraulic transport properties from the triggering front, it has been assumed that they are independent of time and pressure. However, fluid injections can strongly change permeability of rocks (e.g. hydraulic fracturing). In this paper, we investigate what kind of diffusivity estimates are provided by the triggering front in cases where hydraulic transport properties are functions of pore-fluid pressure (i.e. they are changing during the injection). In this case, the pressure relaxation results in a non-linear pore-fluid pressure diffusion associated with heterogeneously and time-dependent distributed permeability. We consider numerically two models of pressure-dependent hydraulic diffusivity, a power law and an exponential law. We generate synthetic microseismicity by solving corresponding 1-, 2- and 3-D non-linear diffusion equations. Our results show that the triggering front provides reasonable estimates of the effective diffusivity approximately corresponding to the hydraulic diffusivity resulting from the stimulation (including hydraulic fracturing) of rocks and thus constitute a significant conceptual update of the seismicity-based reservoir characterization approach.

Soil water characteristic curve measurement without bulk density changes and its implications in the estimation of soil hydraulic properties

During soil water characteristic curve (SWCC) measurement, bulk density has been found to deviate from in situ values largely because soil first swells during saturation and then shrinks during dehydration. To obtain the actual in situ soil hydraulic properties, there is a need for a method to measure soil hydraulic properties at the in situ bulk density. In this study, we proposed a new method to measure soil hydraulic properties (SWCC and saturated hydraulic conductivity ( K s )) at the in situ bulk density encountered. Bulk density changes during SWCC measurements based on a centrifuge method for loam and clay were characterized, and their effects on soil hydraulic properties at constant bulk densities (expressed with a suffix of “-C”) were investigated. Over the pressure head range from 0 to 10,200 cm during conventional SWCC measurement of the centrifuge method, the bulk density increased by 0.49 and 0.40 Mg m − 3 for loam and clay, respectively. K s -C decreased by three and four orders of magnitude for loam and clay, respectively, when the corresponding bulk density increased from 1.20 to 1.59 and from 1.35 to 1.54 Mg m − 3 . The increase in bulk density during conventional SWCC measurement changed the shape of the SWCC-C. The modeled unsaturated hydraulic conductivities at constant bulk densities presented approximately one and two orders of magnitude differences when the bulk densities increased from 1.40 to 1.59 and from 1.45 to 1.54 Mg m − 3 for loam and clay, respectively. This type of investigation provides a method to obtain soil hydraulic properties at in situ bulk densities and, thus, should aid our understanding of in situ soil water and solute transport.

Sparse geologic dictionaries for subsurface flow model calibration: Part I. Inversion formulation

Inference of heterogeneous rock properties from low-resolution dynamic flow measurements often leads to underdetermined nonlinear inverse problems that can have many non-unique solutions. The problem is usually regularized by reducing the number of unknown parameters and/or incorporating direct or indirect prior information. In subsurface flow modeling, structural connectivity in hydraulic properties plays a critical role in determining local and global flow and displacement processes. When reliable prior information about the structural connectivity of a formation is available it can be used to discourage implausible inversion solutions. In this two-part paper, we introduce a geologically-inspired conceptual framework, geologic dictionaries, for reconstructing complex subsurface physical properties from the flow data. We evaluate the performance of our method under both reliable and highly uncertain prior knowledge and measurements. In Part I, we present inversion with sparse geologic dictionaries, learned from prior models, for estimation of complex heterogeneous subsurface hydraulic properties. We show how learning methods can be adapted to build, from a prior training library, specialized sparse geologic dictionaries that contain relevant structural elements (words) for constructing the solution of subsurface flow inverse problems. The key property of the constructed geologic dictionaries that we invoke during flow data integration is its “sparsity”; that is, we require that only a small subset of geologic dictionary elements be sufficient for accurately approximating any prior model realizations in the training library, and hence the model calibration solution. Using the sparsity property of the geologic dictionary, we formulate and solve the nonlinear model calibration as a feature estimation problem. To construct a solution, we adopt an iteratively reweighted least-squares (IRLS) algorithm to identify the important dictionary elements by minimizing a sparsity-regularized data misfit objective function. We illustrate the flexibility and effectiveness of the proposed method by applying it to a series of numerical experiments in multiphase flow systems and compare it with parameterization by truncated singular vectors.

Combined Transient Method for Determining Soil Hydraulic Properties in a Wide Pressure Head Range

Simulation of water flow in unsaturated soils requires knowledge of the soil hydraulic properties. Two standard methods for the simultaneous determination of the water retention and hydraulic conductivity function in the laboratory are the multistep-outflow and the evaporation method. The multistep-outflow method provides information primarily in the pressure head range corresponding to relatively moist conditions, whereas the evaporation method has its highest information content in the medium to dry range. In the moisture ranges not covered by the experiments, an extrapolation of the soil hydraulic functions leads to very uncertain and possibly incorrect estimations of hydraulic properties. To obtain reliable estimates of soil hydraulic properties in a wider range of soil moisture contents, we combined both methods in a successive manner. The combined experiment starts with a multistep-outflow experiment, which is directly followed by an evaporation experiment. We tested this experimental design using synthetic data and laboratory measurements evaluated by inverse modeling. In the evaluation of the combined experiment, data points for the retention and conductivity functions were calculated from the evaporation experiment and included in the objective function for the inverse simulation of the multistep-outflow experiment. The combined evaluation led to greatly improved estimates of the hydraulic properties in a wide moisture range, circumvented the unreliable extrapolation beyond the pressure head ranges of the separate experiments, and significantly reduced the model error caused by such extrapolations.

Feasibility of Sequential and Coupled Inversion of Time Domain Reflectometry Data to Infer Soil Hydraulic Parameters under Falling Head Infiltration

Accurate estimation of soil hydraulic properties is a prerequisite for efficient soil and water management. On a small scale, time domain reflectometry (TDR) measurements obtained during an infiltration event can be used for estimating soil hydraulic properties either using a sequential or a coupled inversion approach. In the traditional sequential approach, the TDR measurements are inverted into water content averages based on travel time analysis and subsequently used for calibrating a hydrologic model. Travel time analysis has been reported to be subjective and difficult to use for analyzing TDR measurements obtained during infiltration. In this paper, we extend the sequential inversion approach by using water content profiles (WCPs) obtained via inverse modeling of TDR measurements and introduce a coupled inversion approach which directly uses the TDR measurements for constraining the inversion for hydraulic properties without first inverting them into WCPs or averages. By comparing the feasibility of these approaches to infer three Mualem-van Genuchten (MVG) hydraulic parameters (α, n, Ks) from TDR measurements obtained under falling head infiltration, we concluded that the coupled approach is more practical and less uncertain than the sequential approach. In particular, the coupled inversion approach allows to simultaneously monitor ponding depth and water infiltration, which avoids the laborious task of manually measuring the ponding depths and can thus enable rapid estimation of the soil hydraulic parameters for multiple locations through automatic measurements of ponded infiltration for multiple rings through TDR multiplexing.

Integration of microseismic and other post-fracture surveillance with production analysis: A tight gas study

Quantitative production analysis of tight gas reservoirs has historically been a challenge due to complex reservoir characteristics (ex. lateral and vertical heterogeneity, stress-sensitivity of permeability and porosity), induced hydraulic fracture properties in vertical wells (ex. multi-phase flow, conductivity changes, complex fracture geometries), operational complexities (ex. variable back-pressure, liquid-loading) and data quality (infrequent rate or flowing pressure reporting). All of these challenges conspire to make extraction of reservoir ( kh and OGIP) and hydraulic fracture properties ( x f and fracture conductivity) soley from production/flowing pressure data difficult, often resulting in non-unique answers. In recent history, there has been the added complication that tight gas (and most recently shale gas) reservoirs are now being exploited with horizontal wells, often stimulated using multiple hydraulic fracture stages, which imparts greater complexity on the analysis. Flow regime identification, which is critical to the correct analysis, is more complicated than ever owing to the number of possible flow regimes encountered in such wells. A case study is presented in which it is demonstrated that modern post-fracture surveillance data, such as microseismic and post-frac production logging, aids in both model identification and model calibration, which is critical to the analysis of hydraulically-fractured horizontal wells completed in tight gas formations. A workflow is presented in which offset vertical wells (to the horizontal wells) are first analyzed to obtain estimates of kh and hydraulic fracture properties, followed by commingled stage and single-stage production analysis of the multi- (transverse) hydraulic fracture horizontal wells. Microseismic data is incorporated into the analysis of the horizontal wells to 1) understand the orientation and degree of complexity of the induced hydraulic fractures and 2) constrain interpretations of effective hydraulic fracture lengths from production data analysis. It is also demonstrated that once the commingled stage analysis of the horizontal wells is completed, the total interpreted effective hydraulic fracture half-length may be allocated amongst the stages using a combination of production logs and tracer logs. The primary contribution of the current work is the presentation of workflows, emphasizing the integration of various data sources, to improve production analysis of multi-frac’d horizontal wells completed in tight gas formations. In addition to the workflows, it is shown that a combination of advanced production analysis approaches, including methods analogous to classic pressure transient analysis, production type-curve matching and simulation, may be necessary to arrive at a unique analysis.

Study on the selection of unsaturated flow model for the different types of soil and soft rock

Precise estimation of unsaturated hydraulic properties of porous media is indispensable in various study areas, such as analyzing the moisture flow, the drying process occurring from the surface, and the pollutant migration beneath the ground surface. Although many empirical/theoretical models describing the unsaturated hydraulic properties have been proposed by several previous researchers, the best model for the different types of soil/rock may not be identical. Thus, the model selection process and the estimation technique of the parameters included in the models should be developed. In the present study, the inverse technique based on the transient evaporation change was investigated to select the model and estimate the model parameters. The experimental work was based on a relatively low permeable soft rock and a relatively high permeable sandy soil (Toyoura standard sand). Experimental equipment was developed to precisely measure the evaporation rate for the high permeable sandy soil. The Genetic Algorithm (GA) was adopted in the inverse technique as an optimization tool. In order to simplify the problem, only the drying process from the saturated condition was considered. It was established that the information concerning the transient evaporation change could be used for the model selection and parameter estimation. Further, the saturation distribution could be used for the selection of the models. The present study provides important information for the development of the model selection process.

Simplified Multistep Outflow Method to Estimate Unsaturated Hydraulic Functions for Coarse‐Textured Soils

Although the multistep outflow (MSO) method is well suited for the estimation of soil hydraulic properties by inverse solution techniques, this method has not been widely adopted because it requires advanced instrumentation and is time consuming. The objective of this study was to develop a modified version of the multistep outflow technique that largely simplifies laboratory procedures and reduces costs and time. The numerical inversion procedures require applying user‐friendly HYDRUS software to estimate fitting parameters for soil water retention and unsaturated hydraulic conductivity curves. Whereas values of saturated water content and saturated hydraulic conductivity must be measured independently, the remaining functional parameters are estimated using an inverse solution of a transient drainage experiment using multiple suction steps and a hanging water column, with drainage outflows measured during drainage. A comparison test showed that the simplified experiment without tensiometric measurements provided sufficient information in the parameter identification compared with a traditional pressure outflow experiment with tensiometric measurements for an Oso Flaco sand and a loamy sand field soil in the suction range of 0 to 17 kPa.

Numerical modeling of the effect of variation of boundary conditions on vadose zone hydraulic properties

An accurate estimation of hydraulic fluxes in the vadose zone is essential for the prediction of water, nutrient and contaminant transport in natural systems. The objective of this study was to simulate the effect of variation of boundary conditions on the estimation of hydraulic properties (i.e. water content, effective unsaturated hydraulic conductivity and hydraulic flux) in a one-dimensional unsaturated flow model domain. Unsaturated one-dimensional vertical water flow was simulated in a pure phase clay loam profile and in clay loam interlayered with silt loam distributed according to the third iteration of the Cantor Bar fractal object Simulations were performed using the numerical model Hydrus 1D. The upper and lower pressure heads were varied around average values of -55 cm for the near-saturation range. This resulted in combinations for the upper and lower constant head boundary conditions, respectively, of -50 and -60 cm, -40 and -70 cm, -30 and -80 cm, -20 and -90 cm, and -10 and -100 cm. For the drier range the average head between the upper and lower boundary conditions was set to -550 cm, resulting in the combinations -500 and -600 cm, -400 and -700 cm, -300 and -800 cm, -200 and -900 cm, and -100 and -1,000 cm, for upper and lower boundary conditions, respectively. There was an increase in water contents, fluxes and hydraulic conductivities with the increase in head difference between boundary conditions. Variation in boundary conditions in the pure phase and interlayered one-dimensional profiles caused significant deviations in fluxes, water contents and hydraulic conductivities compared to the simplest case (a head difference between the upper and lower constant head boundaries of 10 cm in the wetter range and 100 cm in the drier range).