Reservoir Management Strategies Research Articles

Inbreeding intensifies sex‐ and age‐dependent disease in a wild mammal

The mutation accumulation theory of senescence predicts that age-related deterioration of fitness can be exaggerated when inbreeding causes homozygosity for deleterious alleles. A vital component of fitness, in natural populations, is the incidence and progression of disease. Evidence is growing for natural links between inbreeding and ageing; between inbreeding and disease; between sex and ageing; and between sex and disease. However, there is scant evidence, to date, for links among age, disease, inbreeding and sex in a single natural population. Using ecological and epidemiological data from a long-term longitudinal field study, we show that in wild European badgers (Meles meles) exposed naturally to bovine tuberculosis (bTB), inbreeding (measured as multilocus homozygosity) intensifies a positive correlation between age and evidence of progressed infection (measured as an antibody response to bTB), but only among females. Male badgers suffer a steeper relationship between age and progressed infection than females, with no influence of inbred status. We found no link between inbreeding and the incidence of progressed infection during early life in either sex. Our findings highlight an age-related increase in the impact of inbreeding on a fitness-relevant trait (disease state) among females. This relationship is consistent with the predictions of the mutation accumulation theory of senescence, but other mechanisms could also play a role. For example, late-life declines in condition, arising through mechanisms other than mutation accumulation might have increased the magnitude of inbreeding depression in late life. Whichever mechanism causes the observed patterns, we have shown that inbreeding can influence age-dependent patterns of disease and, by extension, is likely to affect the magnitude and timing of the late-life declines in components of fitness that characterise senescence. Better understanding of sex-specific links between inbreeding, disease and ageing provides insights into population-level pathogen dynamics and could influence management strategies for wildlife reservoirs of zoonotic disease.

Using Thermodynamics to Predict the Outcomes of Nitrate-Based Oil Reservoir Souring Control Interventions.

Souring is the undesirable production of hydrogen sulfide (H2S) in oil reservoirs by sulfate-reducing bacteria (SRB). Souring is a common problem during secondary oil recovery via water flooding, especially when seawater with its high sulfate concentration is introduced. Nitrate injection into these oil reservoirs can prevent and remediate souring by stimulating nitrate-reducing bacteria (NRB). Two conceptually different mechanisms for NRB-facilitated souring control have been proposed: nitrate-sulfate competition for electron donors (oil-derived organics or H2) and nitrate driven sulfide oxidation. Thermodynamics can facilitate predictions about which nitrate-driven mechanism is most likely to occur in different scenarios. From a thermodynamic perspective the question “Which reaction yields more energy, nitrate driven oxidation of sulfide or nitrate driven oxidation of organic compounds?” can be rephrased as: “Is acetate driven sulfate reduction to sulfide exergonic or endergonic?” Our analysis indicates that under conditions encountered in oil fields, sulfate driven oxidation of acetate (or other SRB organic electron donors) is always more favorable than sulfide oxidation to sulfate. That predicts that organotrophic NRB that oxidize acetate would outcompete lithotrophic NRB that oxidize sulfide. However, sulfide oxidation to elemental sulfur is different. At low acetate HS− oxidation is more favorable than acetate oxidation. Incomplete oxidation of sulfide to S0 is likely to occur when nitrate levels are low, and is favored by low temperatures; conditions that can be encountered at oil field above-ground facilities where intermediate sulfur compounds like S0 may cause corrosion. These findings have implications for reservoir management strategies and for assessing the success and progress of nitrate-based souring control strategies and the attendant risks of corrosion associated with souring and nitrate injection.

Applied method for production design optimization under geologic and economic uncertainties in shale gas reservoirs

In the last decades innovations in horizontal drilling and hydraulic fracturing enabled production of commercial quantities of natural gas from many unconventional reservoirs. Reservoir management and development strategies for shale and tight gas plays have evolved from ad hoc approaches to more rigorous strategies that involve simulation and numerical optimization in presences of multiple economic and production objectives, constraints and uncertainties. Application of an automated integrated optimization framework for placement of transverse hydraulic fracture stages along horizontal wells might increase shale gas reserves and revenue from unconventional assets even further. However, the uncertainty present in many reservoir properties and economic parameters might have tremendous effect on success or failure of an unconventional project. In this study, we assess the uncertainty in key parameters using a full factorial design of experiment and a stepwise regression analysis and combine this assessment with an optimization strategy based on a genetic algorithm to identify the effects of uncertainty on optimal production design and revenue of an unconventional project.

Assessment of Sedimentation in Wlingi and Lodoyo Reservoirs: A Secondary Disaster Following the 2014 Eruption of Mt. Kelud, Indonesia

Wlingi and Lodoyo reservoirs in the Brantas River basin, Indonesia, provide numerous benefits including reliable irrigation water supply, flood control, power generation, fisheries and recreation. The function of both reservoirs particularly in relation to flood control has declined due to severe sedimentation that has reduced their storage capacities. The sedimentation in Wlingi and Lodoyo reservoirs is mainly caused by sediment inflow from the areas most affected by ejecta from eruptions of Mt. Kelud, one of the most active volcanoes in Indonesia. The main objective of this research is to assess the sedimentation problem in Wlingi and Lodoyo reservoirs, particularly as they are affected by eruptions of Mt Kelud. We performed reservoir bathymetric surveys and field surveys after the most recent eruption of Mt. Kelud in February 2014 and compared the results with surveys undertaken before the eruption. The assessment revealed that both reservoirs were severely affected by the 2014 eruption. The effective storage capacity of Wlingi reservoir in March 2013 was 2.01 Mm3and the survey in May 2015 indicated that the effective storage of Wlingi reservoir had decreased to 1.01 Mm3. Similarly, the effective storage capacity of Lodoyo reservoir in March 2013 was 2.72 Mm3, reduced to 1.33 Mm3in May 2015. These findings underpin the analysis of the impacts of the secondary disaster due to reservoir sedimentation following the volcanic eruption and the implications for mitigating and managing the risks for sustainable use of reservoirs to control floods, supply water, generate electricity, etc. To cope with the extreme sedimentation problem in Wlingi and Lodoyo reservoirs, diverse sediment management strategies have been applied in these reservoirs and their catchments. However sediment disaster management strategies for both reservoirs, an integral part of the Mt. Kelud Volcanic Disaster Mitigation Plan, require continuous maintenance and recurrent operations, and ongoing evaluation and improvement.

Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique

Accurate determination of the bubble point pressure (BPP) is extremely important in several applications in oil industry. In reservoir engineering applications the BPP is an essential input for the reservoir simulation and reservoir management strategies. Also, in production engineering the BPP determines the type of the inflow performance relationship that describes the reservoir production performance. Accurate estimation of the BPP will eliminate the risk of producing in two-phase region. Current correlations can be used to determine the BPP with high errors, and this will lead to poor reservoir management. Artificial intelligent tools used in the previous studies did not disclose the models they developed, and they stated the models as black box. The aim of this research is to develop a new empirical correlation for BPP prediction using artificial intelligent techniques (AI) such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time we extracted the weights and the biases from AI models and form a new mathematical model for BPP prediction. The results obtained showed that the ANN model was able to estimate the BPP with high accuracy (correlation coefficient of 0.988 and average absolute error percent of 7.5%) based on the specific gravity of gas, the dissolved gas to oil ratio, the oil specific gravity, and the temperature of the reservoir as compared with ANFIS and SVM. The developed mathematical model from the ANN model outperformed the previous AI models and the empirical correlations for BPP prediction. It can be used to predict the BPP with a high accuracy (the average absolute error (3.9%) and the coefficient of determination ( $$R^{2})$$ of 0.98).

Introduction to this special section: Business impact of 4D seismic

Since its introduction as a new technology in the mid-1980s, the adoption and deployment of 4D seismic methods has been phenomenal. 4D seismic was developed initially as a geophysical technology to answer key questions related to reservoir development and recovery methods in the North Sea, but early successes resulted in expanding the “customer base” from geoscientists to reservoir, production, drilling, and project engineers as well as management. Today, high-quality 4D interpretations are used to constrain and improve subsurface simulation models, which include both the reservoir and the overburden. In turn, 4D has become an integral part of key business decisions that optimize both selection of new well targets and the overall reservoir management strategy.

Streamline-based history matching of bottomhole pressure and three-phase production data using a multiscale approach

Reconciling geological models to the available dynamic information, commonly known as history matching, is an essential step for optimizing reservoir management and field development strategies, including improved recovery methods. There are several challenges in the current history matching workflow, particularly for high resolution geologic models with multimillion cells and complex geologic architecture. Streamline-based inverse modeling has shown great promise in this respect because of computational efficiency and analytic calculation of sensitivity of production response to reservoir properties. However, the current streamline-based approach has mostly been applied to history matching water-cut and tracer response in two-phase flow.More recently streamline-based sensitivity calculations have been extended to bottomhole pressure and gas-oil ratio. This allows for generalization of streamline-based history matching to the three-phase. In this paper we present an integrated application of the streamline-based three-phase history matching by incorporating water-cut, gas-oil ratio and bottomhole pressure data while updating high resolution geologic models. The crux of our approach lies in the analytic computation of well response sensitivities based on streamline which allows for efficient inversion of production and pressure data. We also validate the accuracy and efficiency of the streamline-based bottomhole pressure sensitivities by comparison with an adjoint-based method using a finite-difference simulator. In the history matching workflow, we incorporate the streamline-based method within a multiscale framework to account for the disparity in resolution of different types of history data. This integrated method proposed in this paper leads to capturing of the large- and fine-scale heterogeneity while matching the pressure and production responses efficiently.We demonstrate the power and utility of the streamline-based approach using synthetic and field applications with three phases. The synthetic example involves the SPE9 benchmark field case with waterflooding and aquifer drive. The field example involves full-field history matching of the Norne Field in the North Sea using water-cut, gas-oil ratio and bottomhole pressure data. The proposed multiscale workflow clearly demonstrates the power and advantage of our approach in achieving geologically consistent history matching at the full-field level.

Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 182719, “Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning,” by R.G. Hanea, SPE, P. Casanova, and L. Hustoft, Statoil; R.B. Bratvold, SPE, University of Stavanger; and R. Nair, C. Hewson, SPE, O. Leeuwenburgh, and R.M. Fonseca, TNO, prepared for the 2017 SPE Reservoir Simulation Conference, Montgomery, Texas, USA, 20–22 February. The paper has not been peer reviewed. Uncertainty assessment and reduction are often elements of high-quality decision making, although they are not, in themselves, value creating. Value can be created only through decisions, and any decision changes resulting from assisted history matching should be modeled explicitly. This paper presents a comparison of existing work flows and introduces a practically driven approach, referred to as “drill and learn,” using elements and concepts from existing work flows to quantify the value of learning (VOL). Introduction The idea to apply numerical optimization methods to reservoir models in order to arrive at optimal field-development plans has been around for a long time. Early methods for optimization were quite limiting, however, in terms of the complexity of the problems that could be addressed. Recent developments in algorithms and computing power have made it possible to begin to address the full complexity of the field-development optimization problem, including a large number of decision variables of various types, a better characterization of geological uncertainty, handling of realistic platform constraints, and operating strategies of newly drilled wells. Closed-loop reservoir management aims to incorporate new information into models and optimize field-development or reservoir-management strategies on a nearly continuous basis. The main assumption underlying the closed-loop-reservoir-management framework is that acquiring information can change decisions about how the field should be developed or operated such that certain performance objectives are improved. This assumption is identical to that underlying the concept of value-of-information (VOI) determination, which addresses the question of whether one should actually acquire specific data considering not only the expected effect on the system performance but also the cost of acquiring these data from which the information is to be extracted. A first attempt to investigate the feasibility of applying a decision analytic VOI work flow to quantitative reservoir-model-based decision making tried to evaluate the increase in expected economic value with and without the use of specific data with associated measurement errors. The probabilistic aspect of a VOI evaluation was accounted for by the use of an ensemble of models that capture geological uncertainty, and the value was based on applying optimization methods to the model ensemble and evaluating the resulting strategies on synthetic truth models that were not part of the ensemble. Improvements to the computational complexity of the originally proposed work flow were suggested in subsequent work. Still, even with the suggested modifications, the computational cost of a formal VOI evaluation would remain prohibitive for many real-field models without further modification or simplification.

Linear Decision Rules for Hydropower Scheduling Under Uncertainty

We investigate the hydropower scheduling problem, in which a price-taking producer determines a reservoir management strategy that maximises the present value of revenues from selling the produced electricity in a well-functioning market. Uncertainty is present both in market prices and in reservoir inflows. To solve the problem, we apply linear decision rules, which is an approximation method for solving multistage stochastic linear programming problems. Traditional methods for solving these types of problems suffer from computational efforts that grow exponentially with the number of stages and state variables. By restricting the decision variables to be affine functions of the realisations of the uncertain parameters, the original intractable problem is transformed into a problem with short computational time. The aim is to investigate feasibility of the framework. The approach is demonstrated on four Norwegian hydropower plants using recent inflow and price data over a ten year time horizon. We obtain flexible reservoir management strategies, providing feasible solutions where a deterministic approach fails, and otherwise improving expected profits by up to 4.5% compared to a deterministic approach. Solutions times are in the order of minutes.

Adaptive modeling of waterflooding process in oil reservoirs

Waterflooding is a common solution for enhancing oil recovery in oil reservoirs in which water is injected to the reservoir. For this purpose, injection rates should be determined to optimally control the desired oil production from each well to accomplish reservoir management strategies. This goal can be achieved by precisely modeling the waterflooding phenomena. In this paper, an applicable algorithm for modeling the waterflooding process in oil reservoirs has been developed. Using proper System Identification techniques will help to consider any change in the inherent reservoir specifications in addition to operating point variations due to injection alternation rate and also severe nonlinear nature of the reservoir and consequently to obtain adaptive model for the reservoir which is able to reflect all the mentioned phenomena. The reservoir has been considered as a Multi-Input/Multi-Output (MIMO) plant with several manipulated variables and outputs to mimic the variables dynamics of the reservoir during its operational life. A Recursive Least Square (RLS) approach has been utilized with proper forgetting factor and sliding window length selection to extract the reservoir dynamic model in the content of a MIMO model structure. The developed approach has been evaluated for SPE10 model as a well-known reservoir case study in this paper.The observed results demonstrate that the proposed method has acceptable capability, compared to the conventional off-line Least Square (LS) and Extended Least Square (ELS) approaches which may lead to Auto Regressive model with eXogenous input (ARX) and Auto Regressive-Moving-Average model with eXogenous inputs (ARMAX) structures, for modeling the dynamical behaviors of the reservoir to be employed as a powerful tool for control and optimization applications.

Prestack 3D and 4D seismic inversion for reservoir static and dynamic properties

We present a successful case study in which prestack 3D and 4D simultaneous AVO inversion is used in conjunction with rock-physics analysis to estimate saturation and pressure changes in a West Africa brownfield using time-lapse (4D) seismic as input. We show that, in 4D seismic, there can be many competing production effects that can be difficult to disentangle using traditional 4D interpretation methods, such as amplitude differences between the base and monitors. This begs the need for a more sophisticated approach to decouple these competing effects, such as the use of prestack simultaneous 3D and 4D inversions. Multiple substack seismic data are used to estimate a variety of 3D and 4D petroelastic attributes for mapping static and dynamic reservoir properties with the primary objective of influencing the continuous infill drilling and the overall reservoir management strategy. Facies-specific low-frequency models were used as priors for the 3D inversion, while velocity changes from time-lapse time shifts were used as priors for the 4D inversion. We also demonstrate the use of rock-physics templates coupled with a lithology-specific Gassmann fluid-substitution method to establish a nonlinear regression-based rock-physics model that obeys bound theory from classical rock physics and honors single and multimineral fluid-substitution theory. The resulting templates, when integrated with the prestack AVO-inversion technique, produce a set of attributes that accurately explain the time-lapse production effects observed on seismic.

Robust optimisation of well placement in geologically complex reservoirs

In deepwater developments, economic constraints impose stringent limits on the number of wells that can be drilled without compromising economic profitability. Thus, optimal placement of wells has a significant impact on project rewards. A robust reservoir management strategy calls for rigorous techniques that account for subsurface uncertainty in geologically complex deepwater reservoirs. We develop a robust well-location optimisation method that is particularly suitable for such reservoirs. A steepest-ascent algorithm that takes advantage of distributed parallel computing constitutes the computational core of the optimisation method. The method operates in parallel on a number of probable reservoir models and maximises the expected value of ultimate recovery factor. A realistic reservoir-model ensemble with a thin oil-rim layout and complex lobate turbidite stratigraphic architecture is utilised for demonstrating the concept. Results validate the auspicious nature of the proposed robust well-location optimisation method for recovery forecasting in stratigraphically complex reservoirs.

Regional distribution and temporal changes in density and biomass of Didymosphenia geminata in two Mediterranean river basins

This study aimed to strengthen the knowledge of Didymosphenia geminata distribution in Spain, and to determine which environmental variables were related to its regional distribution and temporal changes in growth and production in two Ebro River tributary basins (Iregua and Najerilla¸ La Rioja Region). Living cells of D. geminata were present in 40% of the studied sites of both basins but only four of the sites developed massive growths. The diatom is widely distributed only in mountain areas of both basins (above 690 masl), which have low average annual air temperature (8.27 ± 0.28 oC) and low water Soluble Reactive Phosphorus (SRP) concentration (average value 0.024 ± 0.007 ppm). Massive growths occur in sites with high light intensity, hydrological regulation of river flow and the lowest measured SRP concentrations (below 0.018 ppm SRP). We carried out an intensive spatio-temporal survey in the Lumbreras River (Iregua basin), where the first massive growth was detected in La Rioja Region. The highest cell densities (1.6 × 105 cells cm−2) and biomass values (480 gDWm−2) were recorded in late summer. An increase in solar radiation and slight rise in water temperature were associated with an increase in biomass over time, but always in waters with low SRP concentration, the key factor that enhances massive growth development. The alteration of the natural streamflow in the Lumbreras River by the Pajares Reservoir and the fact that water is discharged from the hypolimnion (which decreases the summer river water temperature) are important factors in understanding why D. geminata shows such an increase in biomass in a Mediterranean river. This paper provides new insights into the importance of different factors controlling D. geminata massive proliferations in the Mediterranean climate zone, where it is assumed that the species should not develop such massive growths, compared to temperate zones where the majority of previous studies have been carried out. We suggest that changing the reservoir management strategy would be a very important contribution to the control of such significant developments.

Evaluation of the Factors Controlling Concentration of Non-Condensable Gases and Their Possible Impact on the Performance of Geothermal Systems: Case Study of Olkaria Wells in the Kenyan Rift Valley

The Olkaria geothermal field is located in the Kenyan Rift valley, about 120 km from Nairobi. Development of geothermal resources in the Olkaria area, a high temperature field, started in the early 1950s. In the subsequent years numerous expansions have been carried out with additional power plants being installed in Olkaria. These include a binary plant at Olkaria South West (Olkaria III) in 2000, a condensing plant at Olkaria North East (Olkaria II) in 2003, another binary plant at Olkaria North West (Oserian) in 2004 and finally condensing plants in the year 2014 within East production field (EPF) and Olkaria Domes (OD) areas. The total generation from this field is about 730 Mw. The study considered samples from 4 producing wells from 3 fields of the Olkaria geothermal area (OW-44 from the Olkaria East, OW-724A from the Olkaria North East, and OW-914 and OW-915 from the Olkaria Domes field). The chemical data were first analyzed using SOLVEQ. This helped in the determination of the equilibrium state of the system, the reservoir temperatures and the total moles to be run through CHILLER. The run CHILLER considered the processes that have been proven to be occurring in the Olkaria field i.e., boiling and condensing processes, fluid-fluid mixing rocks and titration resulting from water-rock interaction. The effects on gas evolution were evaluated based on the resulting recalculated gas pressures. The results indicate that the gas species are not in equilibrium with the mineral assemblages. The CHILLER evaluation shows boiling as the major process leading to the evolution of gases. OW-44 had the least gas concentrations, arising from the considered reservoir processes due to degassing, and near surface boiling, besides the removal of NH3, H2 and H2S are through the reaction with steam condensate. The gas breakout is most likely in OW-914 and least in OW-44. The study proposes different reservoir management strategies for the different parts of the Olkaria geothermal field. That is by increasing hot reinjection in the eastern sector around well OW-44. The reservoir around OW-914 is to be managed by operating the wells at a minimum flow rate (or even to close them) or the use of chemical inhibitors to prevent calcite scaling.

Impact of Surface Storage on Reclaimed Water: Seasonal and Long Term

The objectives of this project were to develop (1) a better understanding of the effects of storage on reclaimed water quality, (2) a methodology to help understand/predict water quality changes during storage, and (3) effective management tools for minimizing water quality problems. The research team reviewed approximately 120 published articles, conducted a gray literature survey to analyze the impact of surface storage on reclaimed water quality. The team also evaluated federal guidelines for reclaimed water and developed a brief update on what individual states are doing. It was determined that state and federal water quality objectives can be met at the treatment site. However, because of the seasonal nature of reclaimed water use, water often must be stored in open reservoirs, where changes occur that can affect water quality. The nature of these changes was evaluated, including physical, chemical, and biological processes. The research team evaluated several reservoir management strategies to improve water quality, and reviewed water quality models to assess their applicability for open reclaimed water storage reservoirs. It also developed procedures to evaluate and select management strategies and reservoir water, along with matrices to distill the information learned in the study into a useful format for risk assessors and water quality managers. These tools will enable users to readily equate their specific storage reservoirs to representative examples, and to identify actions most applicable to their specific reclaimed water systems. This title belongs to WERF Research Report Series ISBN: 9781843396383 (Print) ISBN: 9781780403267 (eBook)

Influence of fracture-matrix interaction on thermal front movement in fractured reservoir

Abstract Commercial exploitation of geothermal resources requires the disposal of large volumes of cooled brine in an environmentally acceptable way. Reinjection of cooled (i.e. spent) geothermal water has become a standard reservoir management strategy. Since injected fluids are typically much colder than the reservoir rock, this strategy results in the cooling of the region around the injection wells. Injected cool water may not have sufficient residence time in the reservoir to receive enough heat from surrounding hotter rock, resulting in temperature decrease at producing wells. Usually, the energy transport in geothermal reservoirs is calculated by use of sophisticated numerical simulators. In this paper we present analytical solution of simplified model of mass and heat transfer in fractured porous medium in one dimension, assuming constant rock temperature and neglecting small-scale effect connected with dispersion and heat conduction. The solution presented in this paper is applicable if thermal capacity of rock is high but the specific area is not sufficient for instant thermal equilibrium. This approach allows for better understanding the relation between fluid movement along the fractures and heat transfer between the rock matrix and fluid. Simple numerical experiments reported in the paper have shown the importance of specific surface area of naturally fractured rock, which influences the rate of exchange of heat between the fractures and the rock matrix.

Comprehensive methodology for detecting fracture aperture in naturally fractured formations using mud loss data

Abstract When encountered with naturally fractured formations, the knowledge of the location and the aperture of the fractures intersecting the wellbore has a strong technical and economical impacts on drilling engineering, oil production and reservoir management strategies. So far no method has been well-developed for predicting fracture aperture to optimize particle size of lost circulation materials (LCMs) based on mud loss data, which is the primary source of information retrieved from the drilling process. The typical curve in traditional analytical model, which has been the predominant tool in the assessment of fracture aperture in the past several decades, has two major deficiencies: (1) no consideration of the mechanical properties of fracture and (2) incapable of providing accurate fracture aperture in the case of fracture networks. Based on the mathematic model previously published in JPSE (Xia et al., 2015. J. Pet. Sci. Eng. 129, 254–267), this paper develops a methodology that optimizes the fracture characteristics based on recording mud loss data. The methodology poses the prediction problem as history matching, where the outer optimization shell involves the length and the aperture of the fractures intersecting the wellbore. The proposed methodology systematically integrates a Fracture Generation Module, a Mud Loss Simulator, and a Global Optimization Module. The suggested methodology is automated and accurate for field development. We end this paper with some case studies to illustrate the applicability of this methodology.

Diazotrophic bacterial community variability in a subtropical deep reservoir is correlated with seasonal changes in nitrogen.

Nitrogen-fixing microorganisms (diazotrophs) play important roles in aquatic biogeochemistry and ecosystem functioning. However, little is known about the spatiotemporal variation of diazotrophic microbial communities in deep subtropical reservoirs. In this study, denaturing gradient gel electrophoresis (DGGE), clone libraries, quantitative PCR, and quantitative reverse transcription (RT)-PCR were used together to examine the vertical and seasonal patterns of diazotrophic microbial communities based on nitrogenase (nifH) gene sequences in the Dongzhen Reservoir, China, across time (every 3 months for 1 year) and space (five different water depths). In general, the numbers of DGGE bands increased with water depth during the stratification seasons (spring, summer, and autumn), with the clone-library-based operational taxonomic unit (OTU) number and nifH gene diversity being highest in autumn (6 OTUs at depth 0 m; 15 OTUs at 33 m) and winter (12 OTUs at 0 m, 13 OTUs at 33 m) but decreasing drastically in spring (2 OTUs at 0 m, 3 OTUs at 33 m) and summer (3 OTUs at 0 m, 2 OTUs at 33 m). The nifH gene abundance was lowest in the water mixing season (winter average, 5.17 × 10(7) copies/L) but increased in the three other seasons (9.03 × 10(9) copies/L). Cyanobacteria (dominated by filamentous thermophilic cyanobacteria and Cylindrospermopsis raciborskii) were the most dominant diazotrophic group at all depths and seasons, while both alphaproteobacteria and gammaproteobacteria were co-dominant in the bottom waters in autumn and winter. The distinct seasonal and spatial patterns in diazotrophic communities were significantly related to total nitrogen (TN) and ammonium nitrogen (NH4-N) in the reservoir (P < 0.01). Further, TN showed a significant positive correlation with nifH RNA copy number (P < 0.05) and DGGE band number (P < 0.01), whereas the NH4-N was negatively correlated with nifH DNA copy number (P < 0.01) and positively with both RNA/DNA ratio (P < 0.01) and DGGE band number (P < 0.01). Our data indicated that water stratification, mixing, and nitrogen might drive the diazotrophic community structure and activity in complex ways, thereby influencing the aquatic nitrogen cycle. Therefore, adaptive reservoir management strategies should carefully consider the effects of water stratification for protecting drinking water quality and for controlling the potential for diazotrophic cyanobacteria blooms.

Fines Migration in Fractured Wells: Integrating Modeling With Field and Laboratory Data

Summary Production and drawdown data from 10 subsea deepwater fractured wells have been modeled with an analytical model for unsteady-state flow with fines migration. The simulation results and the field data indicated a good match, within 5%. A sensitivity study conducted on initial concentration of fines, flow rate, maximum fines-mobilization velocity, fines distribution, formation damage, and filtration coefficients confirmed that the model-matching parameters are within values reported commonly in the literature. This paper describes the methodology used to integrate the modeling predictions with field and laboratory data to identify probable causes for increasing skins and declining productivity-index values observed in some of the wells under investigation. It discusses the results of an experiment designed to simultaneously assess the effects of pressure depletion and compaction on fines production and permeability with a triaxial-stress apparatus. This is, to the best of our knowledge, the first time an experiment of this nature is reported in the literature. The good match between the modeling and the field data, further validated with laboratory experiments, allows for discussion of long-term predictions on well productivity impacting current reservoir-management strategies and field-development plans.

Balancing ecosystem services with energy and food security – Assessing trade-offs from reservoir operation and irrigation investments in Kenya's Tana Basin

Abstract. Competition for water between key economic sectors and the environment means agreeing allocations is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks firstly to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly, it seeks to show how trade-offs between achievable benefits shift with the implementation of proposed new rice, cotton and biofuel irrigation projects. To approximate the Pareto-optimal trade-offs we link a water resources management simulation model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume-dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for eight objectives covering the provision of water supply and irrigation, energy generation and maintenance of ecosystem services. Trade-off plots allow decision-makers to assess multi-reservoir rule-sets and irrigation investment options by visualising their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against the disturbance of ecosystems and local livelihoods that depend on them. Full implementation of the proposed schemes is shown to come at a high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of "water-energy-food nexus" resource security issues.