Tool For Reservoir Management Research Articles

Improving the accuracy of an integrated watershed-reservoir water quality modeling approach (SWAT and CE-QUAL-W2) in data scarce watersheds

Watershed-reservoir modeling is an essential tool in lake and reservoir management. Despite its importance, accurately modeling these systems remains challenging due to their inherent complexity and the scarcity of reliable forcing data. This study focuses on a watershed and reservoir located in northern Portugal, where data scarcity presents significant challenges for accurate modeling. Therefore, the primary goal of this study was to improve the prediction capability of an integrated watershed-reservoir water quality modeling solution and to establish a robust methodology that can be effectively applied to other cases. The modelling solution proposed in this study computes a daily ratio (per water quality variable) between the watershed reference and load reduction modeling scenarios, obtained with the SWAT model, which is then applied to the observed forcing water quality data of a reservoir water quality model (CE-QUAL-W2). The load reduction scenario increases the watershed municipal wastewater treatment plant’s efficiency from secondary to tertiary and applies a haul-off scenario to the livestock manure, reducing the reservoir input load of Total P, Total N, and BOD5 in the reservoir by 87%,86%, and 83%, respectively. The results obtained with the classical model solution provided an inaccurate conclusion regarding the load reduction scenario output that was corrected with the proposed modeling approach. Overall, after the correction, the load reduction scenario served to considerably improve the reservoir water quality by reducing the Total P and the phytoplankton biomass by 39% and 72%, respectively, while increasing the DO concentration by 27%. The results of this study show that the proposed modeling approach improves the performance of integrated watershed-reservoir modeling systems in data scarce settings and can serve as a useful tool to support decision-making with regard to water resource management and planning at regional and national level.

APPROACH TO CREATION OF GEOLOGICAL AND RESERVOIR SIMULATION MODEL AND METHODS FOR HISTORY MATCHING OF RESERVOIR ENERGY STATE

Under conditions of complex geological structure and substantial share of hard-to-recover oil reserves, reservoir simulation is a key tool in reservoir management. Direct problems solved by reservoir simulation include forecasting of the production performance, control and management of reservoir development, and localization of residual oil reserves. Inverse problem solution is useful for updating the knowledge of geological structure, reservoir quality, and historical production data by means of history matching. The quality of history matching is fundamental to ensure reliable future forecasts and localization of residual oil zones. The main approaches to appropriate history matching rely on analysis and adjustment of well logging and geological parameters, as well as checking and correction of errors, inadequate historical data, application of physics-based and reasonable approaches and tools. The prerequisite for adequate history matching and predictive ability of the resultant reservoir simulation model is the compliance of simulated and actual energy state of the reservoir. In this paper, the initial stage of reservoir simulation is performed, the approach to history matching of reservoir energy state aimed at improving the quality of further predictions is described as exemplified by one of the production areas of Russian oil field. The main causes of poor history match of formation and bottomhole pressures are revealed. These causes are grouped as follows: — geological causes/reservoir quality, which include permeability field distribution, reservoir connectivity, and determination of rock and fluid compressibility;— technical causes (during reservoir simulation), that is model limitations in case of larger areas, the need to use sector models, consideration of buffer region;— technological causes, including inadequate historical data or lack of data, detection of behind-the-casing flows and production casing leaks in wellbore, etc. For each group of factors that have a substantial impact on reservoir pressure, recommendations and methods of energy state history matching on geological and reservoir simulation models are provided, examples are given.

A Fast History-Matching and Optimization Tool and Its Application to a Full Field with More than 1,000 Wells

Summary In this work, we study a waterflood field containing more than 1,000 wells, and the modern field management techniques with full-fidelity 3D geocellular reservoir models become computationally prohibitive. To overcome the difficulty, we developed a novel flow-network data-driven model—the general-purpose simulator-powered network (GPSNet) model—and used it for rapid history matching and optimization. GPSNet includes physics, such as mass conservation, multiphase flow, and phase changes, while maintaining a good level of efficiency. To build such a model, a cluster of 1D connections among well completion points is constructed and forms a flow network. Multiphase fluid flow is assumed to occur in each 1D connection, and the flow in the whole network is simulated by our in-house general-purpose simulator. Next, to effectively reduce the uncertainty, a hierarchical history-matching workflow is adopted to match the production data. Ensemble smoother with multiple data assimilation (ESMDA) plays an important role in reducing the error at each history-matching step. After that, the best-matched candidate is selected for numerical optimization to maximize field oil production with constraints satisfying field conditions. Excellent history-matching results have been achieved on the field level, and good matches have also been observed for key producers. It is also worth mentioning that the history-matching process took a mere 4 hours to finish 1,100 simulation jobs. The successful application of the GPSNet to this waterflood field demonstrates a promising workflow that can be used as a fast and reliable decision-making tool for reservoir management.

Optimization of reservoir release operation using genetic algorithm method

Genetic Algorithms (GA) are highly effective in optimizing hydropower release parameters, identifying global optima in complex systems, and adapting to varying circumstances, making them ideal for managing hydropower conditions. This study employs GA to optimize hydropower release at Wonorejo Reservoir, Indonesia, aiming to balance power generation with flood control, irrigation, and environmental sustainability. The application of GA improved operational efficiency, resulting in a 10.24 % increase in electricity production. Notably, during the rainy season, the improvement was 27.3 % higher than in the dry season, highlighting GA responsiveness to seasonal variations. These findings demonstrate GA potential as a valuable tool for reservoir management.

Microseismic examples, Bakken Formation, Williston Basin

The Bakken and Three Forks unconventional reservoirs of the Williston Basin are prolific producers of oil and gas. The reservoir properties for these formations are similar, having low porosity and permeability. The combined technologies of horizontal drilling and multistage hydraulic fracture stimulation are used to obtain economic production from these tight reservoirs. Hydraulic fracture stimulation is used to open existing fractures or create new fractures to help produce tight oil and gas resources. Passive seismic (microseismic) monitoring of fracture stimulations provides information about induced fracture azimuth, width, length, height, and stimulated reservoir volume. This information is critical in determining sizes of drilling units and spacing of wells, what is contributing to production from a wellbore, hydraulic fracture orientation and other properties. Microseismic information is a very important tool for reservoir characterisation and management. In situ horizontal stresses in the Williston Basin are anisotropic with the maximum horizontal stress being in the northeast direction. Microseismic and other data from the Williston basin suggest that the regional maximum horizontal stress direction is NE SW (N55° to N70°E). Regional fractures with a similar orientation result in strong anisotropy of horizontal permeability with the maximum horizontal permeability being in the same direction. This orientation is confirmed by lineament analysis, fractures observed from oriented core data, borehole breakout data, and regional stress field published data. Operators generally orient their horizontal wells in a north-south or northwest-southeast direction to encounter maximum numbers of fractures and to be somewhat perpendicular to the maximum horizontal stress and permeability (kmax) direction.

Airborne Trace Gas Remote Sensing and Surface Mobile In Situ: A Novel Tool for the Study of Structural Geological Controls from a Producing Oil Field

SummaryAccurate and representative determination of greenhouse gases (GHG) from oil and gas (O&G) production facilities requires high-spatial-resolution data, which can be acquired by airborne imaging spectrometers. However, assessment of nonmethane hydrocarbon emissions, which are far less amenable to remote sensing, requires mobile surface in-situ measurements (e.g., a mobile air quality laboratory).Field in-situ measurements and airborne thermal infrared spectral imagery were acquired for three producing California oil fields (Poso Creek, Kern Front, and Kern River) located next to each other on 14 September 2018. In addition, a profile ascending a nearby mountain collected in-situ data for the Round Mountain oilfield. Plume methane to ethane ratios were consistent within different regions of the field and differed between these fields in a manner related to field geological structures.Data acquired by an airborne thermal infrared imaging spectrometer, Mako, in 2015 and 2018 showed most emissions were from distant plumes in the Kern Front and Poso Creek fields. The spatial distribution of detected plumes was strongly related to faults, particularly active faults, which are proposed to stress infrastructure, leading to higher fugitive emissions and/or emissions from natural migration pathways (seepage). Additionally, the spatial distribution of detected plumes suggested unmapped faults. Thus, high-sensitivity imaging spectroscopy can improve understanding of reservoir geological structures that impact hydrocarbon migration and field operations, highlighting the potential for a novel reservoir management tool.

Assessing the Borehole-to-Surface Electromagnetic (Bsem) Method as a Tool for Active Reservoir Management (Arm) for Geologic Co2 Storage

Assessing the Borehole-to-Surface Electromagnetic (Bsem) Method as a Tool for Active Reservoir Management (Arm) for Geologic Co2 Storage

Analysis of water velocity changes in time-lapse ocean bottom acquisitions - A synthetic 2D study in Santos Basin, offshore Brazil

Time-lapse seismic is an important tool for reservoir management. As seismic data quality improves, there is a drive for moving towards quantitative interpretation, which demands data of higher repeatability. In deep water time-lapse projects, the changes in the speed of sound of the water layer between baseline and monitor acquisitions play a significant role in the data processing. In this work, we study the effects of these water velocity changes, in a scenario of ocean bottom nodes (OBN) acquisition. We perform 2D elastic seismic modeling with high-resolution property models for two distinct survey dates. In each date, different water velocity profiles, measured in field experiments in a Brazilian Pre-salt area, are considered. To incorporate source and receiver non-repeatability, some differences in the acquisition geometries are also tested for each vintage. The synthetic data are compared in terms of time-lapse amplitudes, and repeatability is quantified via the normalized root mean squared attribute (NRMS). For the parameters considered in our study, the deterioration of NRMS seems to be dominated by the non-repeatability of the receivers and by water velocity variations, with source non-repeatability having a lower contribution. We believe the results presented in this work are a first step towards a more robust methodology for time-lapse feasibility studies, which incorporates imaging uncertainties and allows us to understand the contribution of each element that could affect the 4D seismic image quality.

Development of Decision-Making Support Tools for Future Reservoir Management Under Climate and Land Cover Variability: a Case Study

The effects of climate change have been already experienced globally. The uncertainties of climate and land cover changes in the watershed above the reservoir have direct impacts on the inflow into the reservoir and the water resource management that uses reservoir rule curves. This study aims to assess the impacts of climate and land cover changes on the inflow into the Huai Luang Reservoir, Udonthani province during the period of 2018-2067 by using the simulation of two climate scenarios with the data from the PRECIS model under the emission conditions of B2. The prediction of future inflow has used the InVEST model. The improvement of the reservoir rule curves has used the Cultural Algorithm (CA) technique connected with the reservoir system simulation model. Monthly inflow data flowing into the reservoir from the past 33 years has been used to synthesize 1,000 events in order to evaluate the efficiency of the new obtained reservoir rule curves. Therefore, the data results of water shortage and excess water discharge situation have been illustrated in form of frequency, magnitude, duration of events, average amount of water, and maximum amount of water. The results have showed that average rainfall and temperature have tended to increase. The optimal reservoir rule curves improved by the CA technique can effectively reduce the frequency of water shortage situations and the one of the excess water discharge occurring during inflow changes in normal situation, and future situation under emission scenario B2.

Digital platform as a tool for efficient reservoir management

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management.

New modeling paradigms for assessing future irrigation storage requirements: a case study of the Western irrigation district in Alberta

River basin planning in Alberta has relied on the use of computer modeling since the early 1980s. Typical modeling studies rely on a single time step operational framework, where water allocation decisions are made for individual model time steps, without taking into account seasonal forecasts or the corresponding demand hedging rules that are often implemented by farming communities. This kind of modeling often leads to premature depletion of reservoir storage during dry years, producing model results that represent worse decisions than those that irrigators would make by using the rule-of-thumb. This paper critically reviews the current modeling practice, and provides insight into possible improvements in modeling through the use of multiple time step optimization in combination with optimal demand hedging, which is found as part of the model solution. A case study focuses on potential storage expansions in the Western Irrigation District of Southern Alberta. Improvements with the multiple time step optimization approach also shed new light on important water management decisions made in the past and the value of a revised definition of irrigation failure criteria. Finally, the selected modeling approach reveals significant potential for capital cost savings related to future infrastructure development, and suggests that investing in digital infrastructure – better forecasting and reservoir management tools – may be more productive than investment in additional physical infrastructure.

Fracture Seismic: Mapping Subsurface Connectivity

Fracture seismic is the method for recording and analyzing passive seismic data for mapping the fractures in the subsurface. Fracture seismic is able to map the fractures because of two types of mechanical actions in the fractures. First, in cohesive rock, fractures can emit short duration energy pulses when growing at their tips through opening and shearing. The industrial practice of recording and analyzing these short duration events is commonly called micro-seismic. Second, coupled rock–fracture–fluid interactions take place during earth deformations and this generates signals unique to the fracture’s physical characteristics. This signal appears as harmonic resonance of the entire, fluid-filled fracture. These signals can be initiated by both external and internal changes in local pressure, e.g., a passing seismic wave, tectonic deformations, and injection during a hydraulic well treatment. Fracture seismic is used to map the location, spatial extent, and physical characteristics of fractures. The strongest fracture seismic signals come from connected fluid-pathways. Fracture seismic observations recorded before, during, and after hydraulic stimulations show that such treatments primarily open pre-existing fractures and weak zones in the rocks. Time-lapse fracture seismic methods map the flow of fluids in the rocks and reveal how the reservoir connectivity changes over time. We present examples that support these findings and suggest that the fracture seismic method should become an important exploration, reservoir management, production, and civil safety tool for the subsurface energy industry.

Seismic response to evolving injection at the Rotokawa geothermal field, New Zealand

Catalogs of microseismicity are routinely compiled at geothermal reservoirs and provide valuable insights into reservoir structure and fluid movement. Hypocentral locations are typically used to infer the orientations of structures and constrain the extent of the permeable reservoir. However, frequency-magnitude distributions may contain additional, and underused, information about the distribution of pressure. Here, we present a four-year catalog of seismicity for the Rotokawa geothermal field in the central Taupō Volcanic Zone, New Zealand starting two years after the commissioning of the 140 MWe Nga Awa Purua power station. Using waveform-correlation-based signal detection we double the size of the previous earthquake catalog, refine the location and orientation of two reservoir faults and identify a new structure. We find the rate of seismicity to be insensitive to major changes in injection strategy during the study period, including the injectivity decline and shift of injection away from the dominant injector, RK24. We also map the spatial distribution of the earthquake frequency-magnitude distribution, or b-value, and show that it increases from ∼1.0 to ∼1.5 with increasing depth below the reservoir. As has been proposed at other reservoirs, we infer that these spatial variations reflect the distribution of pressure in the reservoir, where areas of high b-value correspond to areas of high pore-fluid pressure and a broad distribution of activated fractures. This analysis is not routinely conducted by geothermal operators but shows promise for using earthquake b-value as an additional tool for reservoir monitoring and management.

Characterisations of the Late Cenomanian reservoirs in Asala Field, East Bahariya concession, Northern-Western Desert, Egypt

ABSTRACTThe study aimed to combine the different available data to understand the subsurface system and the characteristic of reservoirs in Asala Field to represent the vertical and lateral heterogeneity at the well, multi-well and field scale, which could be used as a tool for reservoir management. Seismic interpretation was conducted on the seismic sections that concerned the study area to make a detailed structural interpretation to determine the structural geometry of the horizons. Petro-physical well log analysis of the reservoir rock of Lower and Middle zones of Abu Roash “G” member has been done and mapped. The estimated volume of original hydrocarbon in place of Lower and Middle zones of Abu Roash “G” member has been calculated.

Monitoring geothermal reservoir developments with the Controlled-Source Electro-Magnetic method — A calibration study on the Reykjanes geothermal field

Surface geophysical monitoring techniques are important tools for geothermal reservoir management as they provide unique information on the reservoir development away from boreholes. For magmatic environments, electromagnetic (EM) methods are attractive monitoring tools as they allow to characterize the reservoir and hence potentially monitor changes related to fluid injection/production. Indeed, the electrical resistivity of reservoir rocks is highly dependent on the volume, chemistry and phase of the in-situ geothermal brine (e.g. liquid, vapor, supercritical).Passive EM techniques (e.g. magnetotellurics or MT) are traditionally used for geothermal exploration and a few recent studies have demonstrated its potential for monitoring reservoir development. One of the main challenges is though the presence of cultural noise and/or variability of the Earth magnetic field that can obfuscate the EM signals of interest. We have investigated the benefits and drawbacks of active EM surveying (Controlled-Source EM or CSEM) to tackle this challenge, first with a synthetic study and subsequently with an actual time-lapse survey acquired in 2016 and 2017 over the Reykjanes geothermal field in Iceland before (baseline) and after (monitor) the thermal stimulation of the supercritical RN-15/IDDP-2 geothermal well.The synthetic study showed that for geothermal fields having a resistivity structure similar to the Reykjanes field (i.e. a conductive caprock overlying a more resistive higher temperature reservoir), CSEM and MT measurements can both detect resistivity changes within the deep resistive reservoir, provided that measurement errors are small. Variations in many survey parameters (e.g. errors in receiver position/orientation, differences in recording devices, variations of near surface conditions, external noise) can create significant time-lapse CSEM measurement errors. Our actual time-lapse survey showed that when similar CSEM equipment is used during the baseline and monitor surveys and systematically d-GPS positioned, the remaining key parameter controlling the survey repeatability is the level of external noise. Since the influence of external noise on CSEM data can be artificially reduced (e.g. by increasing the transmitter dipolar moment), it offers the possibility to adapt the survey design to increase the chance of detecting the time-lapse signals of interest. On the contrary, little control is possible on the MT signal to noise ratio and hence repeatability.The time-lapse EM survey acquired over the Reykjanes geothermal reservoir showed indeed that a high CSEM survey repeatability can be achieved with electric field measurements (within a few percent) but that time-lapse MT survey is a challenging task because of the high level of cultural noise in this industrialized environment. To assess the quality of our CSEM dataset, we inverted the data and confronted the resulting resistivity model with the resistivity logged in the RN-15/IDDP-2 well. We obtained a good match up to 2-3km depth, i.e. enough to image the caprock and the liquid-dominated reservoir but not deep enough to image the reservoir in supercritical conditions. To obtain such an image, we had to jointly invert legacy MT data with our CSEM data. On the monitoring aspects, the analysis of changes in electric fields did not allow to identify any CSEM signal related to the thermal stimulation of the RN-15/IDDP-2 well. One possible explanation is the weakness of the time-lapse CSEM signal compared the achieved CSEM survey repeatability as a result of a limited resistivity change over a limited volume within the reservoir. Future reservoir developments in the supercritical reservoir (e.g. hydraulic stimulation, long-term fluid circulation) will most likely generate stronger resistivity variations over a larger volume than during the thermal stimulation of the well. This calibration study provides the basic information for deciding when and how an EM monitor survey must be performed for the monitoring of the Reykjanes geothermal reservoir but also for the definition of the monitoring strategy of similar high-enthalpy geothermal reservoirs.

Value-of-information for model parameter updating through history matching

The oil and gas (O&G) industry spends billions of dollars on data (e.g., production data, seismic data and tracer data) gathering and analysis for the purpose of reducing uncertainty and improving their understanding of the most salient features of the subsurface. Yet, the O&G industry spends minimal effort and investment to assess whether the benefit of this data gathering and analysis exceeds the cost. One form of data gathering and analysis is history matching (HM), which has been an essential reservoir management tool for decades. This paper addresses the value of HM by applying the value-of-information (VOI) framework originally developed in the decision sciences.There are several challenges involved in assessing VOI in HM contexts. Although reservoir management (HM and production optimization) and decision analysis (DA) use many of the same methods, the two domains involve different terminology used in fluid flow modeling and application of state-of-the-art HM and optimization methods. Furthermore, most applications of VOI analysis have focused on static, as opposed to time-dependent, analysis. Finally, some recent publications in the O&G industry that have illustrated and discussed VOI from HM have not been consistent with the original definition of VOI.In this paper, we illustrate and discuss the use of a consistent, DA-based, VOI analysis framework to assess the VOI in HM contexts. In order to make the VOI framework understandable and accessible to both the reservoir management and DA communities, we provide a “bridge” between the nomenclature and terminology used in VOI calculations and that used in state-of-the-art HM and optimization methods.The paper includes four VOI analysis examples. The first illustrates the implementation of the general VOI framework for a simple HM problem. The second illustrates and discusses the difference between the calculations presented by other authors and the standard VOI definition used in the DA community. The third illustrates the implementation of VOI calculations in more realistic settings, including a sensitivity analysis of measurement noise. The fourth illustrates the application of VOI assessment in a case where a reservoir simulation model is involved.

Evaluating the “Gradual Entrainment Lake Inverter” (GELI) artificial mixing technology for lake and reservoir management

ABSTRACTSmith CA, Read JS, Vander Zanden MJ. 2018. Evaluating the “Gradual Entrainment Lake Inverter” (GELI) artificial mixing technology for lake and reservoir management. Lake Reserv Manage. 00:00–00.Thermal stratification is important to the structure and function of lake and reservoir ecosystems. Yet when lakes undergo eutrophication, thermal stratification can exacerbate water quality problems. As a result, lake management has sometimes involved artificial mixing and destratification, though the available technologies are few and costly. It is therefore important to test the efficacy of new technologies when they arise. Here, we evaluate a lake mixing technology, the “Gradual Entrainment Lake Inverter” (GELI), which was used to mix Crystal Lake, Wisconsin, a 34 ha, 21 m deep, dimictic lake during the summer months of 2012 and 2013. To assess the effect of the GELI treatment on thermal regime, we used DYRESM to model thermal conditions in the 2 treatment years but in the absence of treatment. We found GELI treatment slowly reduced stratification and the temperature range of the lake to 4.2 and 5.3 C in each treatment year, on average. Full destratification and oxygenation of the water column prevented fall hypoxia and anoxia. We found efficiency of the GELI averaged 1.9% during treatment, which is higher than efficiencies reported from field applications of bubble plume aeration. We used DYRESM to simulate bubble plume aeration to match the observed destratification from our GELI treatment and estimate aeration would have required 1.4–1.8 times the airflow and power costs of the GELI. Though considerable limitations of the current iteration of this technology exist, these may be reduced in future versions, possibly leading to a practical lake and reservoir management tool.

Seismic assisted history matching using binary maps

This paper addresses the challenge of integrating 4D seismic data with production data in a quantitative manner in order to improve the forecasting ability of a reservoir model and reduce the associated uncertainty. It presents a history matching workflow that has been applied to production data and time lapse seismic data. In this procedure, the production data objective function is calculated by the conventional least squares misfit between the historical data and simulation predictions, while the seismic objective function uses the Current measurement metric between a binary image of saturation change. This approach is implemented on a real field data from the United Kingdom Continental Shelf (UKCS), where uncertain reservoir parameters which consist of global and local parameters are initially assessed. These parameters include flow based multipliers (permeability, transmissibility), volume based multipliers (net-to-gross, pore volume), as well as the end points of the relative permeability curves (critical saturation points). After the initial screening, sensitive parameters are selected based on the sensitivity analysis. An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process. It is found that the primary control parameters for the binary seismic gas match are the permeability and critical gas saturation, while the volumetric parameters are important for the binary seismic water match in this particular reservoir. This approach is compared to seismic history matching using full seismic modelling, preserving all amplitudes. The results demonstrate that the binary approach gives a good match to gas saturation distribution and water saturation distribution, and the reservoir parameters converge towards a solution. The conventional approach does not capture some signals of hardening and softening in the seismic data, and hence in summary, the binary approach seems more suitable as a quick-look reservoir management tool. A unique feature of this study is the application of the binary approach using Current measurement metric for seismic data history matching analysis, as this circumvents the use of the uncertain petroelastic model. This approach is easy to implement, and also helps achieve an effective global history match.

Time-lapse reservoir property change estimation from seismic using machine learning

Time-lapse seismic analysis is an important tool in reservoir management, well planning, and reservoir model updating. Existing 4D close-the-loop methods utilize rock-physics models and simulation models, and typically work well in simple 4D cases such as water flood movement (where the primary change is the saturations), but they face challenges in reliably and quantitatively estimating 4D reservoir property changes when the reservoir dynamics are complex. We demonstrate a data-driven quantitative method that uses machine learning to leverage the inherent physics between time-lapse reservoir property changes and seismic attributes. The method can simultaneously utilize multiple seismic attributes, including attributes derived from prestack seismic, from multiple 4D seismic surveys. It significantly improves the efficiency and reduces the cycle time for quantitative 4D interpretation. We evaluate the method through a study based on synthetic data modeled after a complex North Sea reservoir with long production and injection history. For this reservoir, separation and estimation of the time-lapse pressure and saturation change are the critical objectives. Results show high prediction accuracy for all the reservoir properties including pressure, saturations, and compaction changes for the blind validation test. Estimated maps of all the reservoir property changes match very well with the simulation model in the synthetic study. This work also demonstrates that prestack seismic attributes significantly improved the estimation of 4D pressure and saturation changes.