Feasibility of rainwater harvesting and consumption in a middle eastern Semiarid urban area

Recent developments of Middle Eastern metropolitans, and the related soaring trend of population increase, is contemporary with the impacts of climate changes. This applies extra pressures to the centralized large-scale water treatment and distribution systems. Rainwater harvesting (RWH) for domestic urban activities can be a sustainable option of adapting with the rising demand of soft water in such an arid/semiarid area. A pilot system of rainwater draining and storage was constructed for alleviating parts of soft water scarcity in Mashhad, the second most populous city of Iran. Measurements were collected for two years at the drainage basin outlet and inside of a storage tank, which has been equipped for water harvesting purposes. We performed some preliminary stochastic analysis and evaluated probabilistic properties of the collected dataset, aiming to explain them with respect to the physical characteristics of the RWH system. Data clustering analysis confirmed that the quality of the water may change during rainwater draining and storage in the RWH tank. Particularly, sodium content of the sampled water in the drainage catchment illustrated higher variations, compared with the ones evaluated for the stored water in the reservoir tank. This can confirm that the quality of the stored water in the RWH reservoir is more stable than that obtained for each separate rainfall–runoff event. We assessed the potential of the harvested water in different consumption contexts, in light of some national and international water quality (physicochemical, biological, and toxic pollutants) guidelines. We relied on water quality indices (WQI) to interpret multiparametric properties of the collected rainwater from urban surfaces; consequently, the quality of the harvested water was categorized with moderate to almost good attributes. This makes it well suited for irrigation uses, which can play a relevant role against water shortages in the analyzed semiarid urban region. Otherwise, infiltration and treatments need to be performed if using harvested water for drinking consumptions (of human or livestock), some of which may be costly for local owners/uses. We provide some suggestions for improving efficiency of the system and enhancing the quality of the harvesting water

Role of Methanogenesis and Sulfate Reduction in Lifetime Performance of Hydrogen Storage in Depleted Gas Reservoirs

Geological formations potentially provide suitable options for underground hydrogen storage (UHS). For seasonal operations of UHS, working gas capacity cannot coincide with the total amount of hydrogen stored in a geological formation where key aims include (i) maintaining reservoir pressure, and (ii) avoiding productions of cushion gas (e.g., CO2, CH4, N2) during withdrawal cycles. Otherwise, when considering long-term (or lifetime) UHS scenarios, the kinetics of the chemical reactions associated with mixtures of hydrogen with the fluids residing in the porous formation can evolve to attain equilibrium conditions. Here, we consider lifetime behavior of UHS scenarios and assess uncertainties associated with hydrogen losses due to its conversion into other chemical species. Given the time scales involved, we disregard kinetic behavior of hydrogen consuming reactions while evaluating the loss of underground stored hydrogen due to its conversion to other chemical products at reservoir thermodynamic equilibrium conditions. Our results are tied to (i) shallow, (ii) intermediate, and (iii) deep reservoirs. Our modeling study suggests that hydrogen losses at equilibrium conditions in shallow reservoirs (low temperature/pressure conditions) is around 5% more (on average) than the corresponding losses associated with deep reservoirs (high temperature/pressure conditions)

Seasonal Influences of Boundary Conditions in Coastal Water Quality Variations (Case Study: Northern Zone of Persian Gulf)

Persian Gulf, as an important biological aquatic basin in Middle East, joints via Hormuz Strait to Oman Sea and Indian Ocean. Tide, wind, precipitation, solar radiation and evaporation are main phenomena regarding the oscillation trend of water quality variation in mentioned basin. Moreover, the flow entrance from Arvand River to Persian Gulf influences aforesaid phenomenon, extensively. This research bases on Mt. Mitchell statistics collected in NOAA research vessel observation through the Persian Gulf, Strait of Hormuz and Gulf of Oman. Investigating the variation of shallow water conditions in aforesaid aquatic basin; we analyzed the regional observations and measurements in comparison with the outputs of a numerical model which has been developed based on Navier Stokes partial differential equations. The results argue that baroclinicity and stratification of fluid column are two important events occur and change in Persian Gulf, seasonally. Based on our obtained results, creation of turbulence; and consequently, diffusion of internal waves originate from both occurrence of thermocline through the water environment and variation of this event in space and time. Just the same, this study focuses on effective parameters and elements in creation of thermocline and the related influences of flow entrance from Arvand River. According to the results, we are convinced about creation and existence of more baroclinicity and turbulence in north-eastern coasts of this aquatic basin in comparison with deeper parts; and this event originates from effects of internal flow from Arvand River, related bed stresses and situation and direction of wind sources

Impact of Hysteresis on Relative Permeability in Hydrocarbon Migration

We consider second migration of hydrocarbons and analyse the effect of the uncertainty associated with three-phase relative permeability on reservoir simulation results. The two-dimensional vertical reservoir has a size of 5000 m × 5000 m and it is discretized by 50 × 50 uniform cells. Parameters of relative permeability models are evaluated via a Maximum Likelihood (ML) approach, relying on a set of coreflooding data available from the literature. Uncertainty in ML calibration of the relative permeability model parameters is propagated to the outputs of reservoir simulations within a Monte Carlo (MC) framework. Results are discussed in terms of time evolution of pressure, saturation (of all three phases) values as well as concentrations of the hydrocarbon components. Our results document a clear influence of the ML parameter estimation uncertainties on the reservoir simulations, especially considering local concentrations of hydrocarbon components. Moreover, even though the uncertain parameters follow a Gaussian distribution, outputs of the MC simulations are generally non-Gaussian and display long (positive and/or negative) tails

Investigation of saturation dependency of oil relative permeability during WAG process through linear and non-linear PCA

We characterize three-phase relative permeability data sets available in the literature in terms of basic descriptive statistics, bivariate correlation, as well as linear (PCA), nonlinear (NLPCA) and hierarchical principal component analyses (h-NLPCA). These studies are viewed in the context of the assessment of three-phase oil relative permeabilities for water alternating gas injection (WAG) protocols, where a proper (qualitative and quantitative) analysis of the dependence of observed three-phase oil relative permeability data on fluid saturations is of critical relevance for practical applications. Here, we focus on the characterization of the dependence of three-phase oil relative permeability on an identifiable set of Principal Components. We analyze the relationship between observed core scale three-phase oil relative permeability and input variables which are typically employed in the application of existing effective (pseudo-empirical) models. Input variables include saturations of fluids, saturations ending points, as well as two-phase relative permeabilities obtained from oil-water and oil-gas environments. The use of available prior information about saturation ending points is also discussed in the framework of Constrained Principal Component Analysis (CPCA). Our results show that: (i) the degree of nonlinearity displayed by the relationship between the input variables and three-phase oil relative permeability is in contrast with the fundamental assumptions underlying existing empirical models; (ii) a sigmoid-based empirical model can effectively characterize three-phase oil relative permeability as a function of fluid saturations, saturation ending points and oil relative permeability data collected under two-phase conditions

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Our study is keyed to the development of a viable framework for the stochastic characterization of coreflooding simulation models under two- and three-phase flow conditions taking place within a core sample in the presence of preferential flow of the kind that can be associated with the presence of a system of fractures. We do so considering various modeling strategies based on (spatially homogeneous or heterogeneous) single- and dual-continuum formulations of black-oil computational models and relying on a global sensitivity-driven stochastic parameter calibration. The latter is constrained through a set of data collected under a water alternating gas scenario implemented in laboratory-scale coreflooding experiments. We set up a collection of Monte Carlo (MC) numerical simulations while considering uncertainty encompassing (a) rock attributes (i.e., porosity and absolute permeability), as well as (b) fuid-fluid/ fluid-solid interactions, as reflected through characteristic parameters of relative permeability and capillary pressure formulations. Modern moment-based global sensitivity indices are evaluated on the basis of the MC model responses, with the aim of (i) quantifying sensitivity of the coreflooding simulation results to variations of the input uncertain model parameters and (ii) assessing the possibility of reducing the dimensionality of model parameter spaces. We then rest on a stochastic inverse modeling approach grounded on the acceptance-rejection sampling (ARS) algorithm to obtain probability distributions of the key model parameters (as identified through our global sensitivity analyses) conditional to the available experimental observations. The relative skill of the various candidate models to represent the system behavior is quantified upon relying on the deviance information criterion. Our findings reveal that amongst all tested models, a dual-continuum formulation provides the best performance considering the experimental observations available. Only a few of the parameters embedded in the dual-continuum formulation are identified as major elements significantly affecting the prediction (and associated uncertainty) of model outputs, petrophysical attributes and relative permeability model parameters having a stronger effect than parameters related to capillary pressure

Appraisal of Native Hydrogen Accumulation in Geological Formations under Uncertainty

A key issue related to potential accumulation of native hydrogen (H2) in geological formations is the risk of hydrogen loss due to conversion to other chemical species such as methane (CH4). Our study tries to quantify how uncertainties linked to carbonate-clay reactions (CCR) reflect in evaluation of the geogenic methane generation and the associated losses of native H2. We rely on a modeling workflow developed by Ceriotti et al. (2017) for evaluating geogenic carbon dioxide, CO2, generation through CCR in sedimentary basins. As a showcase, we consider a one-dimensional (vertical) model patterned after a typical sedimentary compaction setting. Such a model provides the dynamics of porosity, temperature, and pressure along the vertical direction. Outputs of the compaction model are viewed as deterministic quantities. We then consider a given mineral composition and focus on the quantification of the parametric uncertainties associated with CCR. This is reflected in the uncertainty related to the values of thermodynamic equilibrium constants of the species involved in CCR and is then propagated onto the ensuing estimated CO2 release. Underground trapping of native H2 is conceptualized upon considering the subsurface as a natural chemical reactor that consumes a mixture of H2 (generated from serpentinization of ultramafic rocks) and CO2 (from CCR) yielding a mixture of H2/ CO2/ CH4. Our analysis considers that (a) complete mixing of the chemical species is attained and (b) geochemical reactions can be evaluated under thermodynamic equilibrium conditions. We then perform a modelling study framed in a stochastic context and relying on a numerical Monte Carlo framework. The latter is aimed at quantifying uncertainty associated with methane production following geogenic hydrogen and carbon dioxide generation. Our results are tied to (i) shallow, (ii) intermediate-depth, and (ii) deep reservoirs. Due to its preliminary nature, the study considers uncertainty solely in the CCR process as well as accumulation reservoir depth/pressure/temperature conditions. Our results suggest that accumulation of H2 in geological formations entails the risk of hydrogen loss due to conversion to CH4 by methanogenesis. They also suggest that deep geological formations (characterized by high temperature and pressure conditions) tend to limit hydrogen loss due to methanogenesis reactions. Thus, exploration of native H2 accumulations could target geological formations where the residing gas has low CO2 concentrations and where the mineralogical composition of reservoir rocks contains low amounts of carbon-bearing minerals. We provide a quantification of native hydrogen losses with the explicit inclusion of a stochastic assessment of some uncertainties linked to the geogenic generation of CO2

Assessment and uncertainty quantification of onshore geological CO2 storage capacity in China

We provide a probabilistic assessment of CO2 storage capacity in major sedimentary basins in China. Our approach embeds constraints associated with the increase of reservoir pore pressure due to injection of CO2 in the presence of resident brine. Pressure build-up must be limited to avoid fault reactivation, caprock failure, and possible leakage, resulting in more conservative estimates of CO2 storage capacity as compared to volumetric estimates. We rely on a numerical Monte Carlo framework considering uncertainty in the values of reservoir size and major geological formation attributes (i.e., absolute permeability, porosity, and reservoir compressibility). Our work shows that 10 major basins can potentially store, on average, 1350 Gt of CO2 during the next 30 years (lower and upper quartiles being 1100 and 1700 Gt of CO2, respectively). This far exceeds the likely amount (up to 175 Gt of CO2) required to be stored by 2050. Our analysis also suggests that 6 basins (located close to the largest emission areas) can store about 93 Gt (on average) of CO2 during the next 30 years. Underground carbon storage in China, coupled with other possible solutions, could meet the aims of the Announced Pledges Scenario (International Energy Agency) to mitigate global warming by 2060. We also perform a global sensitivity analysis to determine how our predictions of storage capacity may be affected by uncertainties in the simulation model input parameters. Moment-based global sensitivity metrics suggest that geological formation attributes are major sources of uncertainty, significantly affecting model outputs and the associated uncertainty

Numerical assessment of water alternating gas practices in the presence of hysteresis effects on relative permeability

Water Alternating Gas (WAG) injection is one of the most successful enhanced oil recovery approaches. Properly accounting for the hysteretic effects of relative permeabilities is a critical issue encountered in numerical simulations of WAG at the mesoscale. Ranaee et al. (2015) proposed a sigmoid-based model for three-phase oil relative permeability, incorporating key physical effects taking place at the pore scale. The model can then be jointly used with the Larsen and Skauge (1998) model, accounting for gas relative permeability hysteresis, to develop a formulation for three-phase relative permeability suitable for reservoir simulation. In this study we illustrate the impact of this joint formulation on a field scale setting through a suite of numerical simulations of WAG injection targeting a reservoir model inspired to real life cases. The analysis is performed by embedding the illustrated relative permeability models in the black oil model implemented in the Matlab Reservoir Simulation Toolbox (Lie et al., 2011). We assume non-hysteretic behavior for water relative permeability under water-wet conditions and characterize it upon relying on corresponding laboratory-scale data. As a baseline, the results are compared against a scenario in the absence of three-phase relative permeability hysteresis. The computational domain is heterogeneous, the spatial distributions of porosity and absolute permeability varying across the ranges of [0.02-0.3] and [0.1-2600 mD], respectively. The model is set at equilibrium conditions, production being driven by three peripheral injectors and five up-dip producers. A given flow rate is assigned to each injector and a target value of liquid production rate is imposed at the producing wells. The numerically evaluated production rates constitute our target state variables. The schedule of the injectors is set to achieve a preliminary waterflooding phase followed by a WAG injection scheme. The latter is implemented by periodically switching the injected phase between water and gas for two injectors, the third injector continuously injecting water. The numerical simulations are performed through a fully implicit discretization of the equations governing the system dynamics. To minimize computational costs, we employ an algebraic multi-grid method and resort to a multi-processor high performance clustered computer system. Our results suggest that hysteretic effects are important across significant portions of the studied reservoir system. Field production responses are associated with a simultaneous increase of ultimate oil recovery and a corresponding decline of the gas-oil ratio when hysteretic effects are included in the simulations

Analysis of the performance of a crude-oil desalting system based on historical data

Our study is keyed to the development of a methodological approach to assess the workflow and performance associated with the operation of a crude-oil desalting/demulsification system. Our analysis is data-driven and relies on the combined use of (a) Global Sensitivity Analysis (GSA), (b) machine learning, and (c) rigorous model discrimination/identification criteria. We leverage on an extensive and unique data-set comprising observations collected at a daily rate across a three-year period at an industrial plant where crude oil is treated through a combination of demulsification/desalting processes. Results from GSA enable us to quantify the system variables which are most influential to the overall performance of the industrial plant. Machine learning is then applied to formulate a set of candidate models whose relative skill to represent the system behavior is quantified upon relying on model identification criteria. The integrated approach we propose can then effectively assist to (a) modern and reliable interpretation of data associated with performances of the crude oil desalting process and (b) robust evaluation of future performance scenarios, as informed by historical data