Small Number Of Wells Research Articles

“Core–core” petrophysical relationships generation for reservoir modeling

Relevance. The need in a detailed analysis of distribution of physical properties of the formation in space. Currently, the parameters connecting the results of responses of geophysical fields and petrophysical studies of core are averaged. On the one hand, this is due to the small number of wells with cores in the fields, and on the other hand, to simplify and speed up calculations in the presence of a large number of wells with geophysical surveys of wells. However, this approach does not allow us to identify the largest number of a particular layer or section characteristics. This, in its turn, may lead to inaccuracies in calculating filtration and capacitance properties. When averaging parameters, the features of formation of the deposit in parts of the field with core sampling are lost. And this is a very big opportunity to more accurately form facies models of deposits. Aims. To generate a map of skeletal density distribution based on core data for the supra-coal strata of a terrigenous oil reservoir; analyze the resulting distribution map, identify areas with increased and decreased density values; assess the degree of change in the porosity coefficient when compared with density values; identify areas of high and low density and trends. Object. Supra-coal strata of terrigenous sediments of one of the layers of an oil field in the Tomsk region. Methods. Analysis of the petrophysical database leads to formation of an idea of the reservoir. Laboratory core studies are the source of the most reliable information about the filtration and reservoir properties of the formation. The analysis technique involves the well-by-well construction of dependencies of petrophysical parameters and determination of the value of the constant density of the skeleton. Additionally, a general relationship is built for all wells to compare values and identify maximum and minimum parameters boundaries, skeletal density distribution map and resulting zones with low- and high-density values analysis. Borehole differentiation of values leads to increased detail of the distribution of the studied parameter and identification of zones with abnormally high and low values for more detailed study and the formation of a conceptual geological model.

On the challenges of global entity-aware deep learning models for groundwater level prediction

Abstract. The application of machine learning (ML) including deep learning models in hydrogeology to model and predict groundwater level in monitoring wells has gained some traction in recent years. Currently, the dominant model class is the so-called single-well model, where one model is trained for each well separately. However, recent developments in neighbouring disciplines including hydrology (rainfall–runoff modelling) have shown that global models, being able to incorporate data of several wells, may have advantages. These models are often called “entity-aware models“, as they usually rely on static data to differentiate the entities, i.e. groundwater wells in hydrogeology or catchments in surface hydrology. We test two kinds of static information to characterize the groundwater wells in a global, entity-aware deep learning model set-up: first, environmental features that are continuously available and thus theoretically enable spatial generalization (regionalization), and second, time-series features that are derived from the past time series at the respective well. Moreover, we test random integer features as entity information for comparison. We use a published dataset of 108 groundwater wells in Germany, and evaluate the performance of the models in terms of Nash–Sutcliffe efficiency (NSE) in an in-sample and an out-of-sample setting, representing temporal and spatial generalization. Our results show that entity-aware models work well with a mean performance of NSE >0.8 in an in-sample setting, thus being comparable to, or even outperforming, single-well models. However, they do not generalize well spatially in an out-of-sample setting (mean NSE <0.7, i.e. lower than a global model without entity information). Strikingly, all model variants, regardless of the type of static features used, basically perform equally well both in- and out-of-sample. The conclusion is that the model in fact does not show entity awareness, but uses static features merely as unique identifiers, raising the research question of how to properly establish entity awareness in deep learning models. Potential future avenues lie in bigger datasets, as the relatively small number of wells in the dataset might not be enough to take full advantage of global models. Also, more research is needed to find meaningful static features for ML in hydrogeology.

Determination of geothermal temperature and temperature gradient via measurements in the production well sump

Data on the geothermal temperature and the temperature gradient of the earth's crust section are necessary for solving a number of fundamental and applied problems. However, the currently used methods for determining the geothermal temperature based on the temperature measured in the wellbore have known disadvantages and can only be used in a small number of wells in the field under study. The article proposes a new method for determining the geothermal temperature and the temperature gradient from the measured temperature distribution in production wells with a long enough sump. It is shown that every other well out of more than four hundred considered wells of oil fields in the Republic of Bashkortostan (Russia) has an appropriate sump. The influence of heat transfer from the working formation, metal casing and natural thermal convection in the borehole fluid on the temperature field in the sump of the production well is studied analytically and numerically. An algorithm for finding an area with undisturbed temperature distribution in the sump of the wellbore is described. The requirements for the choice of a well and corresponding sump section are given, as well as the procedure for the subsequent processing of thermograms, which makes it possible to determine the geothermal temperature and the temperature gradients in the sump. The efficiency of the proposed technique is demonstrated by two cases of field data examples.

Shear-wave velocity estimation based on rock physics modelling of a limestone gas reservoir in the Pannonian Basin

In the Pannonian Basin, especially in Croatia, there are a small number of wells for which acquired shear-wave (S-wave) velocities have been obtained. Often, quantitative interpretation relies only on compressional-wave (P-wave) velocity data, and S-wave velocity must be modelled. S-wave velocity estimation in combination with other petrophysical data is essential for detailed reservoir characterization. P- and S-wave velocities enable seismic modelling of different saturation states in a reservoir. This paper demonstrates a workflow for S-wave velocity estimation where S-wave velocity data are absent in the gas field and neighbouring fields with the same lithology, based on the Kuster–Toksöz and Xu–Payne models applied to the Pannonian Basin limestone reservoir. The results are calibrated with the P-wave velocity obtained from borehole data and the vertical seismic profile (VSP) S-wave interval velocity. Although rock physics models are idealized analogues of real rocks, a very good correlation was obtained between the modelled and measured P-wave velocity, as well as between the modelled S-wave velocity and the VSP interval velocity. The study also illustrates the problem of defining the pore aspect ratio in zones of increasing shale content. Due to the limited research on the limestones of the Pannonian Basin, these results enable a better understanding of the seismic parameters of the Pannonian Basin limestones. The results indicate that the proposed workflow gives an adequate estimation of S-wave velocities.

Deep-learning based surrogate modeling for fast and accurate simulation in realistic 3D reservoir with varying well controls

For many field applications, the need to make production and/or injection allocation among hundreds of wells on a daily or weekly basis for a fixed geological model is not uncommon. In those problems, the computational cost could be very high using traditional numerical simulation. The embed-to-control (E2C) framework provides a new deep-learning-based reduced-order modeling (ROM) framework for much faster predictions of subsurface flow compared to traditional simulation. Existing research on E2C centers around conceptual reservoirs with a small number of wells in 2D Cartesian grids, and a direct application to realistic 3D reservoirs remain a challenge. This paper proposes an improved surrogate model with significant enhancements in the deep-learning network structure, well quantity calculation, and loss function specification. Firstly, we adopt the dense block structure to increase the width of the encoder, and apply the skip connection architecture to use the intermediate information in the decoding process to calibrate the model; secondly, we add dense structure to evolve production information directly in converter instead of using Peaceman's equations as a post-processing procedure that might cause significant errors; thirdly we add the oil and water rate losses to enhance the accuracy of well dynamics. Compared with original model in a complex 2D reservoir with 189 wells, our improved model can reduce the median error of production from 36% to 4%, saturation from 13% to 1.2% and pressure from 4.6% to 0.5%. Finally, our improved model is extended to 3D applied on an actual 3D reservoir with 105 wells. Compared to the simulation results, the improved E2C model is highly accurate with a median average error of 3.1% for production, 1.0% for saturation and 5.9% for pressure. In terms of time cost, our model can achieve approximately 700x speedups on a RTX2060 GPU after training.

CLARİFİCATİON OF STRUCTURAL AND TECTONİC FEATURES İN COMPLEX DEPOSİTS (ON THE EXAMPLE OF GURGAN-DENİZ FİELD)

CLARİFİCATİON OF STRUCTURAL AND TECTONİC FEATURES İN COMPLEX DEPOSİTS (ON THE EXAMPLE OF GURGAN-DENİZ FİELD)

AUTOMATION SYSTEM OF A MANEUVERING OPERATION WITHIN A DRILLING INSTALLATION

The Oil and Gas industry is pursuing major changes in automation, with the aim of reducing the risk involved in operations and increasing efficiency, speeding up processes to reduce costs. Internationally and especially in the Offshore environment, deposits with high productive potential are targeted, which are put into operation through a small number of wells with a high flow rate, due to the complexity of marine drilling. The high costs in this sector of the industry make it necessary to optimize all operational times, thus we very often encounter automation elements in the marine drilling process. This work intends to present the advantages of using the Simulink program package in the simulation of the processes, respectively of the maneuver operation in a drilling installation, to predict their real behavior.

Joint learning for spatial context-based seismic inversion of multiple data sets for improved generalizability and robustness

Seismic inversion plays a very useful role in the detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geologic variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We have developed a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject the spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple data sets are simultaneously used for training, sharing beneficial information among each of the sets. This results in improvement in the generalization performance on all data sets. We have developed a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 data sets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. We compare and contrast our approach to other learning-based seismic inversion methodologies in the literature. On SEAM, we are able to obtain an average mean squared error of 0.0476, the lowest among all other methodologies.

Improvement of Fracture Network Modeling in Fractured Reservoirs Using Conditioning and Geostatistical Method

Abstract The fracture network in hydrocarbon reservoirs plays a major role in reservoir fluid transfer to production wells. Modeling of fracture in fractured reservoir is often done randomly. Modelling is based on image logs and core information. Because the information is available in a small number of wells, the model is not reliable and this problem makes it impossible to predict the correct flow rate and the amount of wells produced. In this study, an algorithm based on primary and secondary data for fracture network modelling in one of the southwest fields of Iran has been presented. The initial data include aperture fracture and fracture density, and secondary data includes petrophysical data, i.e. electrical resistance and resistance logs used to scale-up characteristics of fracture in wells. In this study, we tried to increase the accuracy of modelling by using modelling conditionality on existing and constructed data. Gaussian conditional simulation produces a set of realizations on which non-linear statistics can be readily available. In this way, information was entered into the model in areas where fracture was predicted to exist. Using the turning bands co-simulation method in geostatistic, the fracture characteristics were simulated in wells that were not available. Using the results of the 3D model, the fracture of the reservoir was re-constructed. The results showed that the modelling performed in this study has been able to increase the fracture prediction accuracy and their properties in fracture density by about 9% and in the fracture opening by about 5%.

Reservoir characterization and 3D modeling of the Aptian Alamein Formation in North Razzak area (North Western Desert, Egypt)

Improving exploration and development techniques of fossil fuels are essential for continuous reserves development. Although there are many oil discoveries in the Razzak Area (North Western Desert, Egypt), there are also many dry wells. Therefore, the tectonic setting and reservoirs characterization of the study area has to be fully understood. Twenty 2D highly filtered stack zero-phase seismic sections and logging data of four wells were analyzed. According to seismic sections and structure contour maps (both time and depth), there is one major structural high (horst). It is a part of the regional Jurassic rift system. The NE Jurassic rifts continued during the deposition of the Early Cretaceous strata as growth faults and have been rejuvenated or continued during the closure of the Neotethys Ocean. During the Late Cretaceous, the compressional stress resulting from the collision between African and Eurasian plates has led to the inversion of the Razzak basin, where some segments of the main faults are transformed from normal to reverse and the hanging walls formed plunging drag fold, which was dissected by a set of NW-SE normal faults. Four prospective closures in the Razzak Field were highlighted. Based on integrated petrophysical tools (Gamma Ray, Sonic Log, Resistivity Deep, Neutron Porosity, and Bulk Density) in four wells (NRZK-1, NRZK2, NRZK-3, and NRZK-4), the Aptian Alamein Formation (mainly dolomite) was interpreted to be a good reservoir and can be subdivided into two zones (Upper Alamein and Lower Alamein) with different reservoir quality. In addition, 3D structure, facies, and petrophysical models were built based on integrated geological and petrophysical data. The results indicated that integrating both seismic profiles with logging data, even from a small number of wells, is highly valuable in evaluating reservoir quality and defining future prospective.

How to Meter Agricultural Pumping at Numerous Small-Scale Wells?—An Indirect Monitoring Method Using Electric Energy as Proxy

The large number of users and the small scale of wells greatly complicate monitoring of groundwater abstraction in areas of intensive pumping by numerous smallholders such as in the North China Plain. This paper presents a study in a typical county in the North China Plain. It discusses the application and challenges of an indirect, energy-based approach to groundwater abstraction monitoring. Intensive field experiments at individual wells were carried out to provide a basis for the conversion from electric energy consumption to groundwater abstraction and to explore the feasibility of direct and indirect abstraction monitoring methods in the study area. The results show that the main challenge of electricity-to-water conversion lies in the large spread of conversion factors between wells. The conversion error at an individual well is found to be less than 20%. The same accuracy is achieved on spatially aggregated levels by testing only a small number of wells. Trade-offs can be made to obtain groundwater abstraction estimates at the required accuracy and with reasonable efforts regarding data collection. The analysis shows that energy-based groundwater abstraction monitoring outperforms direct water metering with respect to cost and robustness. It provides satisfactory data accuracy and equitability in regions where irrigation wells are powered by electricity.

Dificuldades em se monitorar os parâmetros microbiológicos de uma marca de água mineral no Estado de São Paulo

O monitoramento dos parâmetros microbiológicos de uma empresa de água minera de porte pequeno é uma tarefa simples porque se tem um número pequeno de poços a serem monitorados na sua área de lavra. Quando se tenta monitorar os parâmetros microbiológicos de marcas globais, muitos obstáculos são enfrentados devido à grande quantidade de poços que cada companhia de água mineral costuma ter. Muitos poços de uma mesma marca de água mineral podem estar localizados em diferentes estados brasileiros. Escolheram-se quatro marcas de águas minerais diferentes de provenientes de empresas de mineração de água mineral para serem monitoradas. A primeira empresa teve 8 poços monitorados e para as outras três empresas monitoraram-se três poços, um para cada empresa. Os seguintes parâmetros microbiológicos foram monitorados durante dias e meses: Escherichia Coli, Coliformes Totais e Bactérias Heterotróficas. As estações climáticas e os vários tipos de embalagens foram considerados neste monitoramento. Todas as marcas analisadas foram adquiridas em estabelecimentos comerciais no estado de São Paulo, no Brasil.

Study on reasonable nozzle size after volumetric fracturing of glutenite reservoir

What kind of drainage system should be adopted after the volumetric fracturing of the glutenite reservoir in the Mahu 1 well area, lack of systematic research. The reasonable choice of the size of the nozzle is the key to determining the flow back of the fracturing fluid and stable production. By comparing the applicability differences of fracturing and drainage systems at home and abroad, this paper analyzes the production experience of neighboring areas and determines the overall principles of drainage system: small displacement principle, stable drainage principle and step-by-step amplification principle. Then, by calculating the rate of change of oil production, analyzing the system well test curve and production curve, and determining a reasonable drainage system, it is concluded that the volume of the glutenite reservoir after fracturing should be 1.5 mm small nozzle, and the pressure is gradually stabilized. The grade is replaced by a 2.5 mm nozzle. Most of the wells are finally stabilized in the 3.0 mm nozzle production. A small number of wells can be properly enlarged to a 3.5mm nozzle for stable production. Under the guidance of this understanding, the horizontal wells in the Mahu 1 well area have basically reached the design capacity, providing theoretical basis and guidance for the production practice after volume fracturing.

Cannabis and residential groundwater pumping impacts on streamflow and ecosystems in Northern California

Cannabis is an emerging agricultural frontier, but due to its quasi-legal status its environmental impacts are poorly understood. Where cannabis is irrigated by groundwater, pumping can lead to streamflow depletion in surrounding streams which may impair other water users or aquatic ecosystems. Here, we investigate the impacts of groundwater pumping for cannabis irrigation at the scale of the watershed, the individual well, and the stream segment, and contextualize by comparing with residential groundwater use. Combining mapped cannabis cultivation and residential structure locations with grower reports of irrigation water sources, we develop distributed estimates of groundwater pumping and associated streamflow depletion caused by cannabis and residential users within the Navarro River Watershed in Northern California (USA). An estimated 73% of cannabis cultivation sites and 92% of residential structures in the watershed rely on groundwater, and groundwater abstraction leads to streamflow depletion during late summer when groundwater is a critical source of baseflow to ecologically important streams. However, streamflow depletion caused by cannabis cultivation is dwarfed by the impacts of residential use, which causes >5 times as much streamflow depletion and is concentrated close to ecologically important stream segments. Focusing on cannabis, a small number of wells (<25%) cause a disproportionate amount of depletion (>50%), and significant predictors for impacts of a well are the annual pumping rate, the distance to the closest stream, and the transmissivity between the well and the stream. Streamflow depletion increases nonlinearly when pumping occurs within 1.2 km of streams, and most cannabis and residential groundwater use is within this critical distance. Given the rapid increase in cannabis cultivation, these results indicate that potential streamflow depletion from groundwater irrigation of cannabis is a current and future concern, and will be superimposed on top of significant depletion already occurring due to residential use in the region studied.

Intermittent Gas Lift Used in Hydrate Mitigation and Flare Reduction in Algeria

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191542, “Hydrate Mitigation and Flare Reduction Using Intermittent Gas Lift in Hassi Messaoud, Algeria,” by Ala Eddine Aoun, SPE, Faouzi Maougal, and Lahcene Kabour, Sonatrach, and Tony Liao, SPE, Brahim AbdallahElhadj, and Sabrina Behaz, Halliburton, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. Hassi Messaoud is a mature oil field with more than 1,100 production wells. Approximately half of the wells are natural flow and the other half use continuous gas lift (CGL) with concentric (CCE) strings. To reduce the usage of the high volume of lift gas, intermittent gas lift (IGL) was selected in a pilot project to evaluate its applicability in the field for wells characterized by high gas/oil ratio (GOR) and without continuous concurrent water injection (with lift gas) to dissolve salt•deposited•downhole. Production Overview Field production began in 1957. During the first 20 years, all production wells were naturally flowing; in 1980, gas lift was introduced into the field development and has remained the only artificial-lift method. Electrical submersible pumping systems have been introduced into the field recently. Of the production wells, 795 are flowing while 376 are shut in for a variety of reasons. Among the flowing production wells, 409 are naturally flowing and 386 are artificial-lift wells. Production fluids are processed by 25 separator stations throughout the field. Gas usually is compressed first at the satellite level; then, at the central processing facilities, the gas is further compressed to the pressure required for injection into the reservoir. Water is treated by the addition of surfactants. The treated water is pumped into the water-injection network to be used in both reservoir-pressure maintenance and salt wash in wells. In the field’s first years, the oil-production rate increased quickly as the number of wells grew. Since 2007, oil production has declined quickly in naturally flowing wells. The steady increase in oil production from gas-lift wells offset the fast production decline in the full field. However, the oil production from gas-lift wells has leveled off during the past 2 years, while the production from naturally flowing wells recovered slightly. The gas lift wells contribute approximately 40% of the field production. Combined Gas Network for•Reservoir Gas Injection and Gas Lift A specific challenge of the Hassi Messaoud field operation is the highly complex gas network, in which the gas-lift network is attached to the gas- injection network for reservoir-pressure maintenance. The gas lift was grown organically out of the reservoir gas-injection network. Initially, when only a small number of wells needed gas lift, this approach might have been cost- effective, but the increasing number of gas-lift wells meant that different methods had to be•considered.

An Intelligent Model for Estimating Relative Permeability in the Abu-Sennan Oil and Gas Fields, Southwestern Egypt

Permeability is arguably the most critical vector flow parameter used in the analysis of hydrocarbon formations. Permeability data are usually obtained from well shut-in tests and core investigations; however, only a small number of wells have well tests or core measurements. In contrast, well logs are often available for most wells in a field. Therefore, techniques that evaluate relative permeability using well logs can be extremely useful. To this end, an effective and thorough model composed of a radial base function neural network has been constructed to predict the relative permeability of formations within the Abu-Sennan Oil and Gas Fields. A total of 105 previously reported relative permeability core data points scattered along the Abu-Sennan Fields were used to construct and evaluate the proposed model. Input parameters for the neural network were wettability, water saturation, irreducible water saturation, porosity and sample depth. The results of the proposed model were compared to reported field data. The results illustrate that the proposed model is able to predict the relative permeability of specific units within the Abu-Sennan Fields with a high correlation coefficient for unnamed data in the model. The proposed model was assessed using sensitivity analysis based on the input parameters.

Automatic Determination of Sedimentary Units from Well Data

The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. Finally, the method is successfully applied to a field study, where the resulting cluster units are found to be comparable to the field interpretation, suggesting a limit between units defined by paleosols rather than close overlying lacustrine levels.

EXPLOITATION OF GAS WELLS IN LATE STAGE OF DEVELOPMENT OF GAS FIELDS

At late stage of development of gas fields they need to solve the specific issues of increasing the production rate of wells and decreasing water cut. The available experience of development of gas and gas condensate fields proves, that the most effective method of removing of water, accumulating in wells, is an injection into the bottom hole zone of foam-forming compositions, based on surfactants. The most technological in the application was the use of solid and liquid surfactants. Installation in wells of lift columns of smaller diameter ensured the removal of liquid from the bottom hole of wells, but after few month of exploitation the conditions of removal of liquid from the bottom hole of wells deteriorate. The technologies of concentric lift systems and plunger-lift systems are used in small number of wells. The basic technology for removal of liquid from bottom hole of gas wells at present time is the technology of treatment of bottom hole of wells with solid surfactants.

Petroleum systems of the deepwater Mozambique Basin

The Mozambique margin is relatively under explored with a small number of wells both onshore and offshore. Nine major gas fields have been identified to date (Brownfield et al., 2012), which are believed to contain a combined more than 150 trillion ft3 of natural gas. The discovery of large natural gas fields in the Rovuma basin represents one of the most exciting exploration successes of recent years and Mozambique is now considered to be a prolific hydrocarbon province (Hollebeek et al., 2015). This exploration study utilizes a modern, regional 2D seismic grid that was acquired in 2013 and processed to depth in 2015. It covers a large proportion of the Mozambique offshore province (Figure 1) and was used to assess hydrocarbon prospectivity in the region. Seven mega-sequences were identified based on stratigraphy from well ties (Uzcategui et al., 2014), the regional geology and seismic character and were interpreted throughout the study area. This interpretation provided a basin-scale, structural framework in which to model the petroleum systems. Structural and stratigraphic plays were mapped, assessed and built into a 3D model that assesses the viability and quality of petroleum systems offshore Mozambique.

Using groundwater age distributions to understand changes in methyl tert-butyl ether (MtBE) concentrations in ambient groundwater, northeastern United States.

Temporal changes in methyl tert-butyl ether (MtBE) concentrations in groundwater were evaluated in the northeastern United States, an area of the nation with widespread low-level detections of MtBE based on a national survey of wells selected to represent ambient conditions. MtBE use in the U.S. peaked in 1999 and was largely discontinued by 2007. Six well networks, each representing specific areas and well types (monitoring or supply wells), were each sampled at 10year intervals between 1996 and 2012. Concentrations were decreasing or unchanged in most wells as of 2012, with the exception of a small number of wells where concentrations continue to increase. Statistically significant increasing concentrations were found in one network sampled for the second time shortly after the peak of MtBE use, and decreasing concentrations were found in two networks sampled for the second time about 10years after the peak of MtBE use. Simulated concentrations from convolutions of estimates for concentrations of MtBE in recharge water with age distributions from environmental tracer data correctly predicted the direction of MtBE concentration changes in about 65% of individual wells. The best matches between simulated and observed concentrations were found when simulating recharge concentrations that followed the pattern of national MtBE use. Some observations were matched better when recharge was modeled as a plume moving past the well from a spill at one point in time. Modeling and sample results showed that wells with young median ages and narrow age distributions responded more quickly to changes in the contaminant source than wells with older median ages and broad age distributions. Well depth and aquifer type affect these responses. Regardless of the timing of decontamination, all of these aquifers show high susceptibility for contamination by a highly soluble, persistent constituent.