Gaisheng Liu

Quantifying mass transfer in permeable media containing conductive dendritic networks

[1] Tree-like branching networks are conduits for mass transport in many natural systems. Because precisely determining conduit-matrix mass transfer requires exact knowledge of dendrite geometries, interdomain mass exchange rates are often described using the concept of effective mass-transfer. We present a direct association between average geometric properties of dendritic networks and mass transfer. We show that the value of the mass-transfer rate coefficient can be estimated directly using a physically based relation that requires no knowledge of the specific network geometry.

NMR Logging to Estimate Hydraulic Conductivity in Unconsolidated Aquifers

Nuclear magnetic resonance (NMR) logging provides a new means of estimating the hydraulic conductivity (K) of unconsolidated aquifers. The estimation of K from the measured NMR parameters can be performed using the Schlumberger-Doll Research (SDR) equation, which is based on the Kozeny-Carman equation and initially developed for obtaining permeability from NMR logging in petroleum reservoirs. The SDR equation includes empirically determined constants. Decades of research for petroleum applications have resulted in standard values for these constants that can provide accurate estimates of permeability in consolidated formations. The question we asked: Can standard values for the constants be defined for hydrogeologic applications that would yield accurate estimates of K in unconsolidated aquifers? Working at 10 locations at three field sites in Kansas and Washington, USA, we acquired NMR and K data using direct-push methods over a 10- to 20-m depth interval in the shallow subsurface. Analysis of pairs of NMR and K data revealed that we could dramatically improve K estimates by replacing the standard petroleum constants with new constants, optimal for estimating K in the unconsolidated materials at the field sites. Most significant was the finding that there was little change in the SDR constants between sites. This suggests that we can define a new set of constants that can be used to obtain high resolution, cost-effective estimates of K from NMR logging in unconsolidated aquifers. This significant result has the potential to change dramatically the approach to determining K for hydrogeologic applications.

Reassessing the MADE direct-push hydraulic conductivity data using a revised calibration procedure

In earlier work, we presented a geostatistical assessment of high-resolution hydraulic conductivity (K) profiles obtained at the MADE site using direct-push (DP) methods. The profiles are derived from direct-push injection logger (DPIL) measurements that provide a relative indicator of vertical variations in K with a sample spacing of 1.5 cm. The DPIL profiles are converted to K profiles by calibrating to the results of direct-push permeameter (DPP) tests performed at selected depths in some of the profiles. Our original calibration used a linear transform that failed to adequately account for an upper limit on DPIL responses in high-K zones and noise in the DPIL data. Here we present a revised calibration procedure that accounts for the upper limit and noise, leading to DPIL K values that display a somewhat different univariate distribution and a lower lnK variance (5.9±1.5) than the original calibration values (6.9±1.8), although each variance estimate falls within the others 95% confidence interval. Despite the change in the univariate distribution, the autocorrelation structure and large-scale patterns exhibited by the revised DPIL K values still agree well with those exhibited by the flowmeter data from the site. We provide the DPIL and DPP data, along with our calibrated DPIL K values, in the supplemental materials. This article is protected by copyright. All rights reserved.

Field Investigation of a New Recharge Approach for ASR Projects in Near-Surface Aquifers.

Aquifer storage and recovery (ASR) is the artificial recharge and temporary storage of water in an aquifer when water is abundant, and recovery of all or a portion of that water when it is needed. One key limiting factor that still hinders the effectiveness of ASR is the high costs of constructing, maintaining, and operating the artificial recharge systems. Here we investigate a new recharge method for ASR in near-surface unconsolidated aquifers that uses small-diameter, low-cost wells installed with direct-push (DP) technology. The effectiveness of a DP well for ASR recharge is compared with that of a surface infiltration basin at a field site in north-central Kansas. The performance of the surface basin was poor at the site due to the presence of a shallow continuous clay layer, identified with DP profiling methods, that constrained the downward movement of infiltrated water and significantly reduced the basin recharge capacity. The DP well penetrated through this clay layer and was able to recharge water by gravity alone at a much higher rate. Most importantly, the costs of the DP well, including both the construction and land costs, were only a small fraction of those for the infiltration basin. This low-cost approach could significantly expand the applicability of ASR as a water resources management tool to entities with limited fiscal resources, such as many small municipalities and rural communities. The results of this investigation demonstrate the great potential of DP wells as a new recharge option for ASR projects in near-surface unconsolidated aquifers.

Importance of a sound hydrologic foundation for assessing the future of the High Plains Aquifer in Kansas

Steward et al. (1) assess the hydrologic and agricultural future of the High Plains Aquifer. We have many concerns about hydrologic aspects of their study and describe the most significant here.

Stochastic Subspace Projection Methods for Efficient Multiphase Flow Uncertainty Assessment

This work introduces an efficient Krylov subspace strategy for the implementation of the Karhunen-Loeve moment equation (KLME) method. The KLME method has recently emerged as a competitive alternative for subsurface uncertainty assessment since it involves simulations at a lower resolution level than Monte Carlo simulations. Algebraically, the KLME method reduces to the solution of a sequence of linear systems with multiple right-hand sides. We propose a Krylov subspace projection method to efficiently compute different stochastic orders and moments of the primary variable response from the zero-order solution. The Krylov basis is recycled to deflate and improve the initial guess for the block and seed treatment of right-hand sides. Numerical results are encouraging to extend the capabilities of the proposed stochastic framework to address more complex simulation models.

An Efficient Stochastic Decomposition Approach for Large-Scale Subsurface Flow Problems

Subsurface formations are of large scales and are inherently heterogeneous at a multiplicity of scales. Significant spatial heterogeneity and a limited number of measurements lead to uncertainty in characterization of formation properties and thus, to uncertainty in predicting flow in the formations. Such uncertainties add another dimension in probability space to the already large-scale subsurface problems. In this work, we develop an accurate yet efficient approach for solving flow problems in large-scale heterogeneous formations. We do so by obtaining higher- order solutions of the prediction and the associated uncertainty of reservoir flow quantities using the moment-equation approach based on Karhunen-Loeve decomposition (KLME). In the KLME approach, the log permeability (lnK) field is first expanded into a multiscale series in terms of orthogonal standard Gaussian random variables with their coefficients obtained from the eigen-decomposition of the lnK covariance. Next, the pressure and velocity fields are all decomposed with perturbation expansions in which each individual term is further expanded into a polynomial series of orthogonal Gaussian random products. The coefficients associated with these series are deterministic and solved recursively from low to high expansion orders. The pressure and velocity moments (such as the means, covariances, and higher moments) can then be calculated from these coefficients using simple algebraic operations. There are two attractive computational features in this new approach. First, all equations for the deterministic coefficients share exactly the same structure as the original equation, which greatly simplifies its implementation as the existing simulators/solvers can be utilized as well as significantly reduces the computation effort as the coefficient matrix remains unchanged and only the right-hand-side vector needs to be updated across different orders. Second, at each expansion order, the equations are independent of each other, which allows for performing massively parallel computation. The new approach is validated and its efficiency and accuracy is demonstrated with traditional Monte Carlo simulations in large-scale three-dimensional subsurface problems.