A Volcanic Reservoir: Integrated Facies Distribution Modeling and History Matching of a Complex Pressure System

2007 ◽  
Vol 10 (01) ◽  
pp. 77-85 ◽  
Author(s):  
Tomomi Yamada ◽  
Yoshiyuki Okano
Keyword(s):  
History Matching ◽  
Model Space ◽  
Training Image ◽  
Real Field ◽  
Stratigraphic Correlation ◽  
Pressure Data ◽  
Porous Network ◽  
Gas Field ◽  
Pressure System ◽  
Lava Domes

Summary A Tcf-class gas field has been producing over several decades in Japan. The reservoir body comprises stacked rhyolite lava domes erupted in a submarine environment. A porous network developed in each dome and rapid chilling on contact with seawater caused hyaloclastite to be deposited over it. Although hyaloclastite is also porous in this field, its permeability has been reduced dramatically by the presence of clay minerals. Impermeable basaltic sheets and mudstone seams are also present. Each facies plays a specific role in the pressure system. Stratigraphic correlation originally identified multiple reservoirs. Gas has been produced almost exclusively from the largest one. However, following 10 to 20 years of production, the pressures within unexploited reservoirs were noticed to have declined at a variety of rates. Unusual localized behavior has also been observed. Because seismic data were not proved particularly informative, we decided to remodel the entire system by specifically using pressure data. We employed a combination of multipoint geostatistics and probability perturbation theories. This approach successfully captured the curved facies boundaries within stacked lava domes while accounting for pressure data by means of history matching to address nonstationarity in the real field. Building a suitable training image is commonly a difficult aspect of multipoint methods and poses particular problems for volcanic reservoirs. It was accomplished here by iteratively adjusting the prototype until satisfactory history matching was achieved with a reasonable number of perturbations. Ambiguous reservoir boundaries were represented stochastically by populating a predetermined model space with pay and nonpay pixels. The modeling results closely simulate measured pressure histories and appear realistic in terms of both facies distributions and reservoir boundaries. They suggest that uneven pressure declines between different units are caused by the tortuous flow channels that connect them. The results also account for the unusual smaller-scale pressure performances observed. The final training image obtained here indicates more intensive spatial variations in facies than previously appreciated. Original gas in place (OGIP) estimates made with 20 equiprobable realizations are scattered within ±15% of the mean value. Estimates of incremental recovery made by drilling a step-out well reveal greater variation than those made by installing a booster compressor, which quantifies a higher associated geological risk.

History Matching Time-Lapse Surface-Gravity and Well-Pressure Data With Ensemble Smoother for Estimating Gas Field Aquifer Support—A 3D Numerical Study

SPE Journal ◽  
2012 ◽  
Vol 17 (04) ◽  
pp. 966-980 ◽  
Author(s):  
M.. Glegola ◽  
P.. Ditmar ◽  
R.G.. G. Hanea ◽  
O.. Eiken ◽  
F.C.. C. Vossepoel ◽  
...  
Keyword(s):  
History Matching ◽  
Numerical Study ◽  
Gravity Data ◽  
Time Lapse ◽  
Added Value ◽  
Pressure Data ◽  
Production Forecast ◽  
Gas Field ◽  
Ensemble Smoother ◽  
Reservoir State

Summary Water influx is an important factor influencing production of gas reservoirs with an active aquifer. However, aquifer properties such as size, porosity, and permeability are typically uncertain and make predictions of field performance challenging. The observed pressure decline is inherently nonunique with respect to water influx, and large uncertainties in the actual reservoir state are common. Time-lapse (4D) gravimetry, which is a direct measure of a subsurface mass redistribution, has the potential to provide valuable information in this context. Recent improvements in instrumentation and data-acquisition and -processing procedures have made time-lapse gravimetry a mature monitoring technique, both for land and offshore applications. However, despite an increasing number of gas fields in which gravimetric monitoring has been applied, little has been published on the added value of gravity data in a broader context of modern reservoir management on the basis of the closed-loop concept. The way in which gravity data can contribute to improved reservoir characterization, production-forecast accuracy, and hydrocarbon-reserves estimation is still to be addressed in many respects. In this paper, we investigate the added value of gravimetric observations for gas-field-production monitoring and aquifer-support estimation. We perform a numerical study with a realistic 3D gas field model that contains a large and complex aquifer system. The aquifer support and other reservoir parameters (i.e., porosity, permeability, reservoir top and bottom horizons) are estimated simultaneously using the ensemble smoother (ES). We consider three cases in which gravity only is assimilated, pressure only is assimilated, and gravity and pressure data are assimilated jointly. We show that a combined estimation of the aquifer support with the permeability field, porosity field, and reservoir structure is a very challenging and nonunique history-matching problem, in which gravity certainly has an added value. Pressure data alone may not discriminate between different reservoir scenarios. Combining pressure and gravity data may help to reduce the nonuniqueness problem and provide not only an improved gas- and water-production forecast and gas-in-place evaluation, but also a more-accurate reservoir-state description.

Robust CO2 Plume Imaging Using Joint Tomographic Inversion Of Distributed Pressure And Temperature Measurements

2021 ◽  
Author(s):  
Changqing Yao ◽  
Hongquan Chen ◽  
Akhil Datta-Gupta ◽  
Sanjay Mawalkar ◽  
Srikanta Mishra ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
History Matching ◽  
Temperature Measurements ◽  
Injection Well ◽  
Co2 Injection ◽  
Pressure Data ◽  
Distributed Temperature Sensing ◽  
Data Set ◽  
Monitoring Well ◽  
Co2 Plume

Abstract Geologic CO2 sequestration and CO2 enhanced oil recovery (EOR) have received significant attention from the scientific community as a response to climate change from greenhouse gases. Safe and efficient management of a CO2 injection site requires spatio-temporal tracking of the CO2 plume in the reservoir during geologic sequestration. The goal of this paper is to develop robust modeling and monitoring technologies for imaging and visualization of the CO2 plume using routine pressure/temperature measurements. The streamline-based technology has proven to be effective and efficient for reconciling geologic models to various types of reservoir dynamic response. In this paper, we first extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The power and utility of our approach are demonstrated using both synthetic and field applications. We first validate the streamline-based DTS data inversion using a synthetic example. Next, the hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warmback analysis of the temperature data. The novelty of this work is the streamline-based history matching method for the DTS data and its field application to the Department of Engergy regional carbon sequestration project in Michigan.

scholarly journals Reservoir Performance Prediction using Integrated Production Modelling (MBAL Software)

2019 ◽  
Vol 8 (4) ◽  
pp. 1484-1489
Keyword(s):  
Performance Prediction ◽  
History Matching ◽  
Material Balance ◽  
Development Planning ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Production Operation ◽  
Gas Field ◽  
Reservoir Performance ◽  
Fluid Properties

Reservoir performance prediction is important aspect of the oil & gas field development planning and reserves estimation which depicts the behavior of the reservoir in the future. Reservoir production success is dependent on precise illustration of reservoir rock properties, reservoir fluid properties, rock-fluid properties and reservoir flow performance. Petroleum engineers must have sound knowledge of the reservoir attributes, production operation optimization and more significant, to develop an analytical model that will adequately describe the physical processes which take place in the reservoir. Reservoir performance prediction based on material balance equation which is described by Several Authors such as Muskat, Craft and Hawkins, Tarner’s, Havlena & odeh, Tracy’s and Schilthuis. This paper compares estimation of reserve using dynamic simulation in MBAL software and predictive material balance method after history matching of both of this model. Results from this paper shows functionality of MBAL in terms of history matching and performance prediction. This paper objective is to set up the basic reservoir model, various models and algorithms for each technique are presented and validated with the case studies. Field data collected related to PVT analysis, Production and well data for quality check based on determining inconsistencies between data and physical reality with the help of correlations. Further this paper shows history matching to match original oil in place and aquifer size. In the end conclusion obtained from different plots between various parameters reflect the result in history match data, simulation result and Future performance of the reservoir system and observation of these results represent similar simulation and future prediction plots result.

Global-Search Distributed-Gauss-Newton Optimization Method and Its Integration With the Randomized-Maximum-Likelihood Method for Uncertainty Quantification of Reservoir Performance

SPE Journal ◽  
2018 ◽  
Vol 23 (05) ◽  
pp. 1496-1517 ◽  
Author(s):  
Chaohui Chen ◽  
Guohua Gao ◽  
Ruijian Li ◽  
Richard Cao ◽  
Tianhong Chen ◽  
...  
Keyword(s):  
History Matching ◽  
Optimization Method ◽  
Global Search ◽  
Real Field ◽  
Production Data ◽  
Finite Difference Approximations ◽  
Production Forecasting ◽  
Novel Method ◽  
Randomized Maximum Likelihood ◽  
Search Capability

Summary Although it is possible to apply traditional optimization algorithms together with the randomized-maximum-likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method to enhance the global-search capability of the distributed-Gauss-Newton (DGN) optimization method and integrates it with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by minimizing a large ensemble of perturbed objective functions in which the observed data and prior mean values of uncertain model parameters have been perturbed with Gaussian noise. Rather than performing these minimizations in isolation using large sets of simulations to evaluate the finite-difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation in which simulation results are shared among different minimization tasks whenever these results are helping to converge to the global minimum of a specific minimization task. To improve sharing of results, we relax the accuracy of the finite-difference approximations for the gradients with more widely spaced simulation results. To avoid trapping in local optima, a novel method to enhance the global-search capability of the DGN algorithm is developed and integrated seamlessly with the RML formulation. In this way, we can improve the quality of RML conditional realizations that sample the approximate posterior. The proposed work flow is first validated with a toy problem and then applied to a real-field unconventional asset. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of data-conditioned realizations can be generated in parallel within 20 to 40 iterations. The computational cost (central-processing-unit usage) is reduced significantly compared with the traditional RML approach. The real-field case studies involve a history-matching study to generate history-matched realizations with the proposed method and an uncertainty quantification of production forecasting using those conditioned models. All conditioned models generate production forecasts that are consistent with real-production data in both the history-matching period and the blind-test period. Therefore, the new approach can enhance the confidence level of the estimated-ultimate-recovery (EUR) assessment using production-forecasting results generated from all conditional realizations, resulting in significant business impact.

Reserves Determination Using Type Curve Matching and Extended Material Balance Methods in the Medicine Hat Shallow Gas Field

1994 ◽  
Author(s):  
S. L. West ◽  
P. J. R. Cochrane
Keyword(s):  
Material Balance ◽  
Poor Quality ◽  
Curve Matching ◽  
Pressure Data ◽  
Gas Reservoirs ◽  
Shallow Gas ◽  
Gas Field ◽  
Type Curve ◽  
Balance Analysis ◽  
Diffusivity Equation

Tight shallow gas reservoirs in the Western Canada Basin present a number of unique challenges in accurately determining reserves. Traditional methods such as decline analysis and material balance are inaccurate due to the formations' low permeabilities and poor pressure data. The low permeabilities cause long transient periods not easily separable from production decline using conventional decline analysis. The result is lower confidence in selecting the appropriate decline characteristics (exponential or harmonic) which significantly impacts recovery factors and remaining reserves. Limited, poor quality pressure data and commingled production from the three producing zones results in non representative pressure data and hence inaccurate material balance analysis. This paper presents the merit of two new methods of reserve evaluation which address the problems described above for tight shallow gas in the Medicine Hat field. The first method applies type curve matching which combines the analytical pressure solutions of the diffusivity equation (transient) with the empirical decline equation. The second method is an extended material balance which incorporates the gas deliverability theory to allow the selection of appropriate p/z derivatives without relying on pressure data. Excellent results were obtained by applying these two methodologies to ten properties which gather gas from 2300 wells. The two independent techniques resulted in similar production forecasts and reserves, confirming their validity. They proved to be valuable, practical tools in overcoming the various challenges of tight shallow gas and in improving the accuracy in gas reserves determination in the Medicine Hat field.

History Matching Using 4D Seismic and Pressure Data on the Norne Field

2005 ◽  
Author(s):  
M. Lygren ◽  
O. Husby ◽  
B. Osdal ◽  
Y. El Ouair ◽  
M. Springer
Keyword(s):  
History Matching ◽  
Pressure Data ◽  
4D Seismic

Estimation of the Location of the Boundary of a Petroleum Reservoir

1975 ◽  
Vol 15 (01) ◽  
pp. 19-38 ◽  
Author(s):  
Wen H. Chen ◽  
John H. Seinfeld
Keyword(s):  
History Matching ◽  
Single Phase ◽  
Steepest Descent ◽  
Petroleum Reservoir ◽  
Pressure Data ◽  
Reservoir Properties ◽  
Matching Problems ◽  
Reservoir Property ◽  
Boundary Location

Abstract This paper considers the problem of estimating the shape of a petroleum reservoir on the basis of pressure data from wells within the boundaries of pressure data from wells within the boundaries of the reservoir. It is assumed that the reservoir properties, such as permeability and porosity, are properties, such as permeability and porosity, are known but that the location of the boundary is unknown. Thus, this paper addresses a new class of history-matching problems in which the boundary position is the reservoir property to be estimated. position is the reservoir property to be estimated. The problem is formulated as an optimal-control problem (the location of the boundary being the problem (the location of the boundary being the control variable). Two iterative methods are derived for the determination of the boundary location that minimizes a functional, depending on the deviation between observed and predicted pressures at the wells. The steepest-descent pressures at the wells. The steepest-descent algorithm is illustrated in two sample problems:the estimation of the radius of a bounded circular reservoir with a centrally located well, andthe estimation of the shape of a two-dimensional, single-phase reservoir with a constant-pressure outer boundary. Introduction A problem of substantial economic importance is the determination of the size and shape of a reservoir. Seismic data serve to define early the probable area occupied by the reservoir; however, probable area occupied by the reservoir; however, a means of using initial well-pressure data to determine further the volume and shape of the reservoir would be valuable. On the basis of representing the pressure behavior in a single-phase bounded reservoir in terms of an eigenfunction expansion, Gavalas and Seinfeld have shown how the total pore volume of an arbitrarily shaped reservoir can be estimated from late transient pressure data at the completed wells. We consider pressure data at the completed wells. We consider here the related problem of the estimation of the shape (or the location of the boundary) of a reservoir from pressure data at an arbitrary number of wells. For reasons of economy, the time allowable for closing wells is limited. It is important, therefore, that any method developed for estimating the shape of a reservoir be applicable, in principle, from the time at which the wells are completed until the current time. Thus, the problem we consider here may be viewed as one in the general realm of history matching, but also one in which the boundary location is the property to be estimated rather than the reserved physical properties. The formulation in the present study assumes that everything is known about the reservoir except its boundary. In actual practice, the reverse is generally true. (By the time sufficient information is available regarding the spatial distribution of permeability and porosity, the boundaries may be fairly well known.) Nevertheless, relatively early in the life of a reservoir, when initial drillstem tests have served to identify an approximate distribution of properties, it may be of some importance to attempt to estimate the reservoir shape. Since knowledge of reservoir properties such as permeability and porosity is at properties such as permeability and porosity is at best a result of initial estimates from well testing, core data, etc., the assumption that these properties are known will, of course, lead only to an approximate reservoir boundary. As the physical properties are identified more accurately, the reservoir boundary can be more accurately estimated. It is the object of this paper to formulate in a general manner and develop and initially test computational algorithms for the class of history-matching problems in which the boundary is the unknown property.There are virtually no prior available results on the estimation of the location of the boundary of a region over which the dependent variable(s) is governed by partial differential equations. The method developed here, based on the variation of a functional on a variable region, is applicable to a system governed by a set of nonlinear partial differential equations with general boundary conditions. The derivation of necessary conditions for optimality and the development of two computational gradient algorithms for determination of the optimal boundary are presented in the Appendix. To illustrate the steepest-descent algorithm we present two computational examples using simulated reservoir data. SPEJ P. 19

Identifiability of Estimates of Two-Phase Reservoir Properties in History Matching

1984 ◽  
Vol 24 (06) ◽  
pp. 697-706 ◽  
Author(s):  
A.T. Watson ◽  
G.R. Gavalas ◽  
J.H. Seinfeld
Keyword(s):  
Pressure Drop ◽  
History Matching ◽  
Single Phase ◽  
Pressure Data ◽  
Relative Permeabilities ◽  
Unknown Parameters ◽  
Reservoir Properties ◽  
Matching Problem ◽  
Two Phase ◽  
Reservoir History Matching

Abstract Since the number of parameters to be estimated in a reservoir history match is potentially quite large, it is important to determine which parameters can be estimated with reasonable accuracy from the available data. This aspect can be called determining the identifiability of the parameters. The identifiability of porosity and absolute parameters. The identifiability of porosity and absolute and relative permeabilities on the basis of flow and pressure data in a two-phase (oil/water) reservoir is pressure data in a two-phase (oil/water) reservoir is considered. The question posed is: How accurately can one expect to estimate spatially variable porosity and absolute permeability and relative permeabilities given typical permeability and relative permeabilities given typical production and pressure data" To gain insight into this production and pressure data" To gain insight into this question, analytical solutions for pressure and saturation in a one-dimensional (1D) waterflood are used. The following, conclusions are obtained.Only the average value of the porosity can be determined on the basis of water/oil flow measurements.The permeability distribution can be determined from pressure drop data with an accuracy depending on the pressure drop data with an accuracy depending on the mobility ratio.Exponents in a power function representation of the relative permeabilities can he determined from WOR data alone but not nearly so accurately as when pressure drop and flow data are used simultaneously. Introduction The utility of reservoir simulation in predicting reservoir behavior is limited by the accuracy with which reservoir properties can be estimated. Because of the high costs properties can be estimated. Because of the high costs associated with coring analysis, reservoir engineers must rely, on history matching as a means of estimating reservoir properties. In this process a history match is carried out by choosing the reservoir properties as those that result in simulated well pressure and flow data that match as closely as possible those measured during production. In general, reservoir properties at each gridblock in the simulator represent the unknown values to be determined. Although there are efficient methods for estimating such a large number of unknowns, it has long been recognized from the results of single phase history matching exercises that many different sets of parameter values may yield a nearly identical match of observed and predicted pressures. The conventional single phase predicted pressures. The conventional single phase history matching problem is in fact a mathematically illposed problem, which explains its nonunique behavior. Such a situation is, in short, the result of the large number of unknowns to be estimated on the basis of the available data and the lack of sensitivity of the simulator solutions to the parameters. Because of this lack of sensitivity, the need to reduce the number of unknown Parameters or to introduce some additional constraints, such as "smoothness" of the estimated parameters, has been recognized. A problem as important as that of choosing which minimization method to employ in history matching is that of choosing, on the basis of the available well data. which properties actually should be estimated. This selection properties actually should be estimated. This selection depends on the relationship of the unknown parameters to the simulated well data. Ideally one would want to knowwhich parameters can be determined uniquely if the measurements were exact, andgiven the expected level of error in the measurements, how accurately we can expect to be able to estimate the parameters. The first question, that of establishing uniqueness of the estimated parameters, is notoriously difficult to answer, and for a parameters, is notoriously difficult to answer, and for a problem as complicated as reservoir history matching, problem as complicated as reservoir history matching, there are virtually no general results available that allow one to establish uniqueness for permeability or porosity. Thus, it is not possible in general to base our choice of which parameters to estimate on rigorous mathematical uniqueness results. In lieu of an answer to Question 1, the selection of parameters to be estimated can be based on Question 2, parameters to be estimated can be based on Question 2, which is amenable to theoretical analysis. If the expected errors in estimation of any of the parameters, or any linear combination of the parameters, are extremely large, then that parameter or set of parameters can be judged as not identifiable. In such a case, steps may be taken to reduce the number of unknown parameters. In summary, the reservoir history matching problem is a difficult parameter estimation problem, and understanding the relationship between the unknown parameters and the measured data is essential to obtaining meaningful estimates of the reservoir properties. Quantitative studies regarding the accuracy of estimates for single-phase history matching problems have been reported by Shah et al. and Dogru et al. Shah et al,. investigated the optimal level of zonation for use with 1D single-phase (oil) situations. SPEJ P. 697

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