Life Cycle Of Reservoir Research Articles

The neglect of the change in inundation area leads to overestimation of carbon emission in cascade reservoirs

Reservoir carbon emissions reflect greenhouse gases emitted by flooded land post-construction. However, pre-construction flooded land has been overlooked in previous assessments. Utilizing annual land cover data from 1990 to 2022 and pertinent parameters of cascade reservoirs in the Lancang River (LCR), we calculated the actual flooded areas of these reservoirs. Subsequently, the Tier1 model was employed to estimate the carbon emissions during the reservoir's life cycle and the annual carbon emissions from newly flooded land during construction. Our findings indicate that the LCR cascade reservoir's carbon emission throughout its life cycle is 4.324Tg CO2eq (1.818–8.879Tg CO2eq). When compared with previous results, our estimated figures (0.496–2.106 g CO2eq/(kw·h)) fall below the global hydroelectric carbon footprint's average threshold range (IPCC: 4–14 g CO2eq/(kW·h)). This implies that the previously estimated carbon emissions from the reservoir may be inflated due to the flooded land prior to reservoir construction. Notably, the nutrient state of the water body predominantly governs reservoir carbon emissions. This research sheds light on the intricacies of carbon emissions from cascade reservoirs and underscores the importance of accurately delineating reservoir boundaries and managing nutrient inputs to mitigate carbon emissions.

Evolution of CO2 Storage Mechanisms in Low-Permeability Tight Sandstone Reservoirs

Understanding the storage mechanisms in CO2 flooding is crucial, as many carbon capture, utilization, and storage (CCUS) projects are related to enhanced oil recovery (EOR). CO2 storage in reservoirs across large timescales undergoes the two storage stages of oil displacement and well shut-in, which cover multiple replacement processes of injection–production synchronization, injection only with no production, and injection–production stoppage. Because the controlling mechanism of CO2 storage in different stages is unknown, the evolution of CO2 storage mechanisms over large timescales is not understood. A mathematical model for the evaluation of CO2 storage, including stratigraphic, residual, solubility, and mineral trapping in low-permeability tight sandstone reservoirs, was established using experimental and theoretical analyses. Based on a detailed geological model of the Huaziping oilfield, calibrated with reservoir permeability and fracture characteristic parameters obtained from well test results, a dynamic simulation of CO2 storage for the entire reservoir life cycle under two scenarios of continuous injection and water–gas alternation were considered. The results show that CO2 storage exhibits the significant stage characteristics of complete storage, dynamic storage, and stable storage. The CO2 storage capacity and storage rate under the continuous gas injection scenario (scenario 1) were 6.34 × 104 t and 61%, while those under the water–gas alternation scenario (scenario 2) were 4.62 × 104 t and 46%. The proportions of storage capacity under scenarios 1 and 2 for structural or stratigraphic, residual, solubility, and mineral trapping were 33.36%, 33.96%, 32.43%, and 0.25%; and 15.09%, 38.65%, 45.77%, and 0.49%, respectively. The evolution of the CO2 storage mechanism showed an overall trend: stratigraphic and residual trapping first increased and then decreased, whereas solubility trapping gradually decreased, and mineral trapping continuously increased. Based on these results, an evolution diagram of the CO2 storage mechanism of low-permeability tight sandstone reservoirs across large timescales was established.

Deep neural network model for estimating montney shale gas production using reservoir, geomechanics, and hydraulic fracture treatment parameters

The development of a shale gas project requires an accurate prediction of the production potential owing to the high cost of horizontal well drilling and hydraulic fracturing treatment. Previous research developed models for estimating shale gas production based on the reservoir and hydraulic fracturing data. They reported a high degree of uncertainty in the prediction as a result of the fracture properties' locking information. This research aims to reduce the uncertainty in shale gas production prediction by developing a deep neural network (DNN) model that incorporates reservoir, geomechanics, and fracture treatment parameters to predict monthly Montney shale gas production. The DNN model, optimized using the Keras Tuner optimization tool, employs a unique architecture of eight hidden layers and integrates production time as an input feature. This allows the model to predict production rates at any time point in the reservoir's life cycle. The prediction model was developed using 102,000 simulation data and blind tested using other 18,000 unseen simulation data, which were achieved by coupling a hydraulic fracture simulator with a reservoir simulator. The model revealed an average error of 13.41%, with a promising correlation between predicted and simulated gas production rates (R2 > 0.85), and cumulative gas production (R2 > 0.89). These results demonstrate the model's accuracy in predicting Montney shale gas production and its utility in assessing the economic viability of shale gas projects. In conclusion, the research introduces an important advancement in reducing uncertainty in shale gas production prediction, providing an accurate tool that has wide applications in assessing the viability of shale gas projects and suggesting further exploration in refining the prediction techniques for other geological formations.

Assessing Geological Deformation Across Spatial and Temporal Scales Using Distributed Fiber Optic Sensing

Summary This work presents a conceptual framework for assessing geological deformation using distributed fiber-optic sensing (DFOS) that is applicable to several common sources of strain encountered during the reservoir life cycle. Common applications include strain associated with seismic and aseismic fault motion, natural and hydraulic fracture dilation and closure, and poroelastic strain evolution during injection and production. We briefly review common geological sources of strain observed in reservoir settings, then discuss the main fiber-based techniques for recording strain with attention to key deformation characteristics at different spatial and temporal resolutions. The relationships between common acquisition parameters, such as spatial resolution, data sampling rate, ability to measure relative and absolute strain, and a priori knowledge of geological strain including geomechanical models, and the availability of baseline measurements are discussed. Finally, a few examples are shown from experimental studies at the Aramco Research Center in Houston, Texas, USA. The facilities host a shallow vertical well instrumented with fiber as well as a surface fiber network embedded in a cement pad. We highlight several data sets acquired using Brillouin and Rayleigh frequency shift (BFS and RFS), low-frequency distributed acoustic sensing (LF-DAS), and DAS interrogation techniques, with a focus on concepts helpful for interpreting field strain. Using these insights as a conceptual framework for assessing geological deformation leads to more informed decisions when planning DFOS acquisitions and interpreting associated strain data.

Application of Autoregressive Integrated Moving Average for Modeling and Forecasting of Crude Oil Production

Most of the crude oil production forecasting and modeling studies have focused on the traditional model of decline curve analysis as techniques used for generating forecast values of future oil and gas in place in conventional and unconventional wells. To many production reservoir economists, the importance of forecast estimations is critical because it allows forecast users to have a profound interest in monitoring and improving forecast performance. It also provides clear indications for the directions, strategies, and bottom line of both the National Oil Companies (NOCs) and International Oil Corporations (IOCs). While the traditional DCS model techniques have well-grounded mathematical underlining. This statistical design does necessarily assure that predicated function regardless of the values of the predictive variables. Moreover, the power traditional oil reservoir production forecasting technique is inefficient. This current research attempts to provide appropriate modeling and forecasting techniques for reservoirs utilizing a time series approach. It reveals how the historical oil production data can be used to project future oil reservoir production and how these projections influence future oil reservoir production decisions. Hence, the main objective of this research study to is practically explore the possibility of the autoregressive integrated-moving average model as a feasible function preference for predicting crude oil production. The historical oil reservoir production time series were used to establish respective autoregressive integrated moving average models through the time series technique by Box–Jenkins and the suitable models were designated with four performance criteria: maximum likelihood, standard error, Schwarz Bayesian criterion, and Akaike criterion for seven elected regions oil reservoir production and the established models conformed to the ARIMA ( p , d , q ). Once the process is identified, the adequacy of the forecasts will be determined and compared with the traditional decline curve analysis. Thus, as an accurate and effective oil production prediction for stretching a reservoir life cycle and enhancing reservoir productivity and recovery factors. These results provide production economists and reservoir engineers with quick, reliable, consistent, and real-time guidelines in budgeting, planning, and making decisions regarding field development. Finally, ARIMA forecasting models are more precise and deliver operational efficiency for dynamic forecasting of oil reservoir production. Keywords: Oil and gas production forecasting, decline curve analysis, time series, ARIMA modeling, DOI: 10.7176/JETP/13-1-05 Publication date: February 28 th 2023

Robust Optimization Technique Using Modified Net Present Value and Stochastic Simplex Approximate Gradient

Summary This article aims to combine, from previous works, a modified objective function and the stochastic simplex approximate gradient (StoSAG) to provide a robust technique that optimizes reservoir production on the basis of a sequence of short-term optimizations. Usually, in reservoir optimization, the main goal is to maximize the net present value (NPV); this work used a modified NPV (MNPV) function that introduces reservoir parameters into the objective function. This MNPV analyzes the relation between cash flow and the reduction of the produced oil fraction, which is an indicator of the reduction of the well production life. On the other hand, the StoSAG is a well-established algorithm for robust optimization, and it was used to perform a constructive optimization with the MNPV cost function. The proposed technique (MNPV), together with StoSAG, is compared with other techniques from the literature using a regular base of all reservoir life cycle and the same proposed short-term optimizations, but using the classical NPV. These comparisons were made based on two benchmarks, SPE9 and Egg reservoir models, with an ensemble of 5 and 100 realizations, respectively. As a result, the MNPV StoSAG presents strong cash flow at the beginning of the reservoir production, a competitive NPV along the entire life cycle, and fast simulation time.

Statistical modeling of oil reservoir life cycle

Based on statistical analysis, a method of stage-by-stage structuring and allocation of the boundaries of the stages of oil field development is proposed. The procedures for calculating the remaining recoverable reserves and the time to reach the maximum level of current oil production are given. Based on the values of accumulated oil production at the final stage of development, using the adaptive Kalman filter, forecasts of oil production are constructed in discrete time and estimates of the possibility of achieving design indicators with the existing development system are given. Keywords: Stage-to-stage structuring; Development stages; Forecast of recoverable reserves; Predicted oil production; Kalman filter.

Statistical modeling of oil reservoir life cycle

Based on statistical analysis, a method of stage-by-stage structuring and allocation of the boundaries of the stages of oil field development is proposed. The procedures for calculating the remaining recoverable reserves and the time to reach the maximum level of current oil production are given. Based on the values of accumulated oil production at the final stage of development, using the adaptive Kalman filter, forecasts of oil production are constructed in discrete time and estimates of the possibility of achieving design indicators with the existing development system are given. Keywords: Stage-to-stage structuring; Development stages; Forecast of recoverable reserves; Predicted oil production; Kalman filter.

Joint Application of Diagenetic, Petrophysical and Geomechanical Data for Selecting Hydraulic Fracturing Candidate Zone

The more comprehensive information on the reservoir properties will help to better plan drilling and design production. Herein, diagenetic processes and geomechanical properties are notable parameters that determine reservoir quality. Recognizing the geomechanical properties of the reservoir as well as building a mechanical earth model play a strong role in the hydrocarbon reservoir life cycle and are key factors in analyzing wellbore instability, drilling operation optimization, and hydraulic fracturing designing operation. Therefore, the present study focuses on selecting the candidate zone for hydraulic fracturing through a novel approach that simultaneously considers the diagenetic, petrophysical, and geomechanical properties. The diagenetic processes were analyzed to determine the porosity types in the reservoir. After that, based on the laboratory test results for estimating reservoir petrophysical parameters, the zones with suitable reservoir properties were selected. Moreover, based on the reservoir geomechanical parameters and the constructed mechanical earth model, the best zones were selected for hydraulic fracturing operation in one of the Iranian fractured carbonate reservoirs. Finally, a new empirical equation for estimating pore pressure in nine zones of the studied well was developed. This equation provides a more precise estimation of stress profiles and thus leads to more accurate decision-making for candidate zone selection. Based on the results, vuggy porosity was the best porosity type, and zones C2, E2 and G2, having suitable values of porosity, permeability, and water saturation, showed good reservoir properties. Therefore, zone E2 and G2 were chosen as the candidate for hydraulic fracturing simulation based on their E (Young’s modulus) and ν (Poisson’s ratio) values. Based on the mechanical earth model and changes in the acoustic data versus depth, a new equation is introduced for calculating the pore pressure in the studied reservoir. According to the new equation, the dominant stress regime in the whole well, especially in the candidate zones, is SigHmax>SigV>Sighmin, while according to the pore pressure equation presented in the literature, the dominant stress regime in the studied well turns out to be SigHmax>Sighmin>SigV.

A Study of Asphaltene Precipitation Problem in some wells in Kurdistan Region

Throughout the production and reservoir lifecycle, the asphaltene precipitation is an ever existing problem through changing the porosity, permeability and wettability leading to decline in production. The conditions that govern Asphaltene precipitation varies from well to well and from reservoir conditions of high pressure and temperature to surface conditions and need to be studied case by case. The modeling and predicting the phase behavior and precipitation of Asphaltene is paramount for wells in Kurdistan region as it is developing its oil and gas industry. Crude oil samples from three wells in Kurdistan Region-Iraq were selected for this study. Experimental data such as crude oil composition using Gas Chromatography, PVT analysis and reservoir pressure and temperature were used as input data into Computer Modeling Group CMG simulator and a model of Asphaltene phase behavior was suggested. The model suggests that the maximum precipitation occurs near the bubble point pressure at reservoir conditions. This is validated and compared with results in literature indicating similar behavior of crude oil. To predict the Asphaltene precipitation at surface condition a modified Colloidal Instability Index CII were used and the results were validated by De Bore plot

A Study of Asphaltene Precipitation Problem in some wells in Kurdistan Region

Throughout the production and reservoir lifecycle, the asphaltene precipitation is an ever existing problem through changing the porosity, permeability and wettability leading to decline in production. The conditions that govern Asphaltene precipitation varies from well to well and from reservoir conditions of high pressure and temperature to surface conditions and need to be studied case by case. The modeling and predicting the phase behavior and precipitation of Asphaltene is paramount for wells in Kurdistan region as it is developing its oil and gas industry. Crude oil samples from three wells in Kurdistan Region-Iraq were selected for this study. Experimental data such as crude oil composition using Gas Chromatography, PVT analysis and reservoir pressure and temperature were used as input data into Computer Modeling Group CMG simulator and a model of Asphaltene phase behavior was suggested. The model suggests that the maximum precipitation occurs near the bubble point pressure at reservoir conditions. This is validated and compared with results in literature indicating similar behavior of crude oil. To predict the Asphaltene precipitation at surface condition a modified Colloidal Instability Index CII were used and the results were validated by De Bore plot

Decameter-Scale Flow-Unit Classification in Brazilian Presalt Carbonates

Summary Rock typing into flow units (FUs) plays a pivotal role in constructing static and dynamic models of petroleum reservoirs. Decisions made by asset teams mostly depend on predictions of how fluids will percolate through the subsurface during the reservoir life cycle. In carbonate settings, dealing with rock typing is complex and can generate a large quantity of units because of diagenetic processes such as dissolution, cementation, and silicification. Most rock-typing methods in carbonates successfully classify small-scale flow heterogeneity in well resolution but fail when interpolating those facies further away from the 1D domain, because of the lack of correlation between FUs and spatial data. Seismic data can be used to detect large-scale FUs and assist the interpolation of small-scale FUs in 3D reservoir volume, thus producing more-realistic static and dynamic models. We propose a modification of the classical rock-typing methods that use permeability (k) vs. porosity (ϕ) plots and electrical properties, with a data set from the Mero Field, part of the giant Libra Field of presalt carbonate reservoirs in offshore Brazil. From the permeability cumulative S-curve analysis, we established major large-scale FUs that maintain part of the carbonate flow heterogeneity and correlate them with the elastic attributes: P-impedance (PI) and S-impedance (SI). In addition, we established a priori PI and SI correlations with the formation-factor (FF) (F) parameter to categorize large-scale FUs using the F vs. k methodology. With the large-scale FUs mapped in seismic data sets, core-plug-scale FUs can be populated into the 3D static and dynamic models using geostatistics tools, thus creating more-realistic reservoir models and assisting asset teams in the decision-making process.

Estimation of Reservoir Sediment Flux through Bottom Outlet with Combination of Numerical and Empirical Methods

Sediment deposition issues for reservoirs are important in Taiwan because the severe deposition could excessively decrease the reservoir lifecycle. Extreme storm events usually can carry a massive amount of sediment into reservoirs, and deposition will happen unless the incoming material can pass through sluice gates. When it comes with high concentration, the density current flow is prone to be generated, and the bottom outlets are the most effective sluice gate to release the sediment. In order to improve the sediment release efficiency, an accurate estimation of arriving concentration and time of the density current can be useful for the reservoir management. This study develops a two-stage approach which combines a numerical model (SRH2D) and the modified Rouse equation to predict the sediment flux of the reservoir. The numerical model was verified and applied to establish the relation between inflow and dam face concentration. The modified Rouse equation then adopted this relation to estimate the proper exponential parameter. As a result, the sediment flux amount at each bottom outlet can be accurately predicted by this equation. With this means, an early warning system can be established for reservoir operation, which can improve release efficiency during typhoons.

How to establish an integrated production management system across the reservoir lifecycle

Successful reservoir management today is inextricably linked with the field’s production system and a complete evaluation of the behavior of that system throughout the reservoir lifecycle. Operator challenges include the need to efficiently design and safely operate production systems for optimum oil and gas delivery under any conditions; the ability to assess a wide variety of scenarios to understand the limits of the production system while still optimizing recovery; and the importance of minimizing downtime by effectively responding to events that impact flow. There is also a need for operators to communicate between multiple technical domains from reservoir characterization, flow simulation and network simulation to processing facilities. Yet, is today’s exploration and production software able to effectively integrate reservoir engineering and production environments and effectively utilize reservoir models so that operators can obtain a complete picture of the field and have a fully effective reservoir and production management solution? This article will examine whether this is being achieved and introduce an integrated production management workflow from Emerson.

Coal permeability: Gas slippage linked to permeability rebound

The main factors/mechanisms that influence coal permeability are effective stress, swelling, shrinkage, deformation and gas slippage. After an extended period of gas production, coal can have a rebound phenomenon where permeability increases with increasing effective stress. This rebound can have a significant impact on gas recovery during the late stages of a reservoir life cycle. This paper aims to characterise coal permeability by combining laboratory measurements with a simple gas slippage model that explains the rebound phenomenon. Gas and Klinkenberg corrected permeabilities of coal are measured at (1) constant confining pressure and (2) constant effective stress. We estimate the length scales relevant to gas flow using mercury intrusion, a permeability slip model, and the kinetic theory of gases, which allows us to estimate the Knudsen number for gas flow. Results show a linear relationship between slip length and the mean free path of gas for all of the tested mean pore pressures. This result suggests that a first order slip boundary condition is sufficient to explain the momentum exchange at the gas/solid boundary during flow under normal reservoir conditions. A correlation between Knudsen number and increased permeability is developed, which further demonstrates that slippage cannot be neglected in coals when Knudsen number is greater than 0.1. Overall, we present a simple model that explains permeability rebound in coal by considering only gas slippage. We do not discredit the mechanism of coal shrinkage, which could also influence coal permeability. We confirm that gas slippage should be considered in coal permeability models.

Real-time management of the waterflooding process using proxy reservoir modeling and data fusion theory

Waterflooding is the use of water injection to enhance the oil recovery in mature oil reservoirs. In this paper an adaptive algorithm has been introduced for waterflooding management in oil reservoirs based on proxy modeling technique. The presented approach is capable to handle the time-varying nature and the inherent nonlinearity of the complex process. In addition, any variation either in market prices or in operational costs is compensated by the designed adaptive controller to fix the obtained profit (here, the net present value: npv) at a desired achievable value. The observed outcomes on 10th SPE-Model#2 benchmark case study have shown that by using this algorithm, any feasible desired trajectory for the expected benefit can be satisfied during the waterflooding-based production. Since the suggested controller has adaptive structure, it can be re-adjusted continuously in each time-step, using available operational data, to take into account the reservoir dynamical variations as well as the external disturbances to present an acceptable performance. By including a monitoring module in the algorithm structure based on data fusion technique, the updated profitability/productivity status of the reservoir is estimated. By using this information the npv setpoint induced to the closed-loop system can be automatically re-adjusted such that it always remains in an acceptable and reasonable range. In conclusion, the proposed methodology is an applicable solution for fairly profit-sharing in different kinds of contracts. In other words, the gained profit can be appropriately allocated to the shareholders according to the contractual obligations or a defined npv trajectory while considering the current condition of the reservoir. This strategy helps to prevent from ultra-production in a specific period of time by the clients or contractors which may lead to an unexpected reduction in the share of other parties in the reservoir life-cycle.

Synergistic recycling of calcined clayey sediments and water potabilization sludge as geopolymer precursors: Upscaling from binders to precast paving cement-free bricks

The building materials industry is facing relevant challenges in terms of sustainability requirements. The same challenge is expected for other anthropogenic activities such as reservoir management. In this study, in the light of industrial ecology approach, two wastes, namely clayey sediments and water potabilization sludge, generated through reservoir life cycle, were used in a synergistic way in the synthesis of sustainable geopolymer binders. In order to guarantee a productive equilibrium between the different yearly evolution of building materials demand and wastes production by the basin, precast materials have been regarded as optimum potential application. In this regard, calcination conditions, mix design and curing conditions were preliminarily optimized. Particularly, geopolymerization kinetics were evaluated by means of mechanical and microstructural characterization of pastes to assess the influence of early age curing conditions and mix design on the engineering performance and, afterwards, the product was upscaled to a typical precast concrete element. The whole set of results demonstrated the feasibility of the proposed recycling route, revealing highly promising perspectives for further studies and broader application field.

Development of an Algorithm for Sustainability Based Assessment of Reservoir Life Cycle Cost Using Fuzzy Theory

Sustainability assessment can be used as a basic managerial analysis to evaluate the capability of water resources in supplying associated demands. Alteration of management policies in order to improve the systems’ performance and sustainability can lead to additional costs of system operation. In this study a novel framework is proposed for assessing economic life cycle costs of dams considering system’s performance from sustainability aspect. For convenience, the term Life Cycle Costs (LCC) is used instead of Economic LCC throughout this paper. First, a fuzzy model is proposed to discount and estimate the LCC of dams in different time intervals of their life span including: Construction, Operation and Maintenance (M&O), and Disposal periods. The model is capable of reflecting uncertainties caused by estimation and previsions of different costs because of fuzzy theory application. Then, sustainability of dam performance in operation period is evaluated through triple criteria of Reliability, Reversibility, and Vulnerability (R-R-V). To provide more realistic results, different system performance levels are defined based on the system’s capability to supply demands and the importance of each level is evaluated by weighting them. Furthermore, it is studied how changes in reservoirs operation strategies can reduce the LCC because of higher performance. The proposed methodology is applied to assess the LCC and performance of a dam located at North Eastern part of Iran. The results show that the system’s performance is remarkably enhanced when the operating rules are revised and this change will intangibly reduce the economic benefits.

Emerging Zoonoses and their Determinants

Zoonotic diseases represent one of the leading causes of illness and death from infectious disease. Worldwide, zoonotic diseases have a negative impact on commerce, travel, and economies. In most developing countries, zoonotic diseases are among those diseases of major public health significance and contribute significantly to an already overly burdened public health system. In industrialized nations, zoonotic diseases are of particular concern for at-risk groups such as the elderly, children, childbearing women, and immunocompromised individuals. The World Health Organization has defined zoonoses as, “diseases and infections naturally transmitted between nonhuman vertebrate animals and humans”, and emerging zoonotic disease as a "zoonosis that is newly recognized or newly evolved or that has occurred previously but shows an increase in incidence or expansion in geographical, host or vector range". However link between humans and animals with respect to diseases could be framed in many but slightly different ways. Strikingly, 75% of emerging infectious diseases have been identified as zoonotic in origin. Moreover if we could link the emergence of some diseases to animals, for e.g. AIDS then the number would be much higher. These agents have included some that maintain an ongoing reservoir life cycle in animals or arthropods, without the permanent establishment of a new life cycle in humans, as well as some “species jumpers” that derive from an ancient reservoir life cycle in animals but have subsequently established a new life cycle in humans that no longer involves an animal reservoir. Zoonotic diseases require rather different prevention and control strategies than diseases of etiologic agents employing only human-to-human transmission. Determinants discussed above have to be understood and dealt in proper perspective when it comes to the problem of zoonotic diseases. Different section of workers should collaborate their efforts against dreaded diseases, which are affecting mankind and animals and are continuously posing challenges. Multidisciplinary teams of ecologists, mammalogists, ornithologists, and entomologists, as well as physicians, epidemiologists, public health workers and veterinarians should join hands for intensive and sure success

Through the continuous life cycle of reservoir, geophysics makes its mark

A quiet revolution has been taking place in the geophysical community. Assisted by better measurements and faster processing, seismic data have become an essential tool for reservoir management. Using seismic to monitor the behavior of reservoirs in production is not a new concept, but it has taken some time to evolve from a theoretical principle to a practical necessity. By developing the ability to perform highly accurate time-lapse seismic surveys, production geophysicists have been able to observe and quantify changes in the seismic data that can only be the result of fluid movement within the reservoir. This provides a heretofore unavailable macro-image of the reservoir's behavior over time that can be used to calibrate sophisticated numerical simulators, long used by reservoir engineers to predict performance.