Combined simulation and inversion of SP and resistivity logs for the estimation of connate-water resistivity and Archie’s cementation exponent

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. E107-E114 ◽  
Author(s):  
Jesús M. Salazar ◽  
Gong Li Wang ◽  
Carlos Torres-Verdín ◽  
Hee Jae Lee
Keyword(s):  
Water Saturation ◽  
Estimation Method ◽  
Inversion Method ◽  
Initial Water ◽  
Data Set ◽  
Spontaneous Potential ◽  
Connate Water ◽  
Resistivity Logs ◽  
Combined Simulation ◽  
Water Resistivity

Knowledge of initial water saturation is necessary to estimate original hydrocarbon in place. A reliable assessment of this petrophysical property is possible when rock-core measurements of Archie’s parameters, such as saturation exponent [Formula: see text] and cementation exponent [Formula: see text], are available. In addition, chemical analysis of formation water is necessary to measure connate-water resistivity [Formula: see text]. Such measurements are seldom available in most applications; if they are available, their reliability may be questionable. We describe a new inversion method to estimate [Formula: see text] and Archie’s cementation exponent from the combined use of borehole spontaneous-potential (SP) and raw array-induction resistivity measurements acquired in water-bearing depth intervals. Combined inversion of resistivity and SP measurements is performed assuming a piston-like invasion profile. In so doing, the reservoiris divided into petrophysical layers to account for vertical heterogeneities. Inversion products are values of invaded and virgin formation resistivity, radius of invasion, and static spontaneous potential (SSP). Connate-water resistivity is calculated by assuming membrane and diffusion potentials as the main contributors to the SSP. Archie’s or dual-water equations enable the estimation of [Formula: see text]. The new combined estimation method has been successfully applied to a data set acquired in a clastic formation. Data were acquired in a high permeability and moderately high-salt-concentration reservoir. Values of [Formula: see text] and [Formula: see text] yielded by the inversion are consistent with those obtained with a traditional interpretation method, thereby confirming the reliability of the estimation. The method is an efficient, rigorous alternative to conventional interpretation techniques for performing petrophysical analysis of exploratory and appraisal wells wherein rock-core measurements may not be available.

APPLICATION OF RESISTIVITY INVERSION MODELLING TO ENHANCE WATER SATURATION CALCULATIONS FROM WIRELINE LOGS

2000 ◽  
Vol 40 (1) ◽  
pp. 355
Author(s):  
C.J. Shield
Keyword(s):  
Capillary Pressure ◽  
Water Saturation ◽  
Strong Relationship ◽  
Data Set ◽  
Hydrocarbon Reservoir ◽  
The Core ◽  
Irreducible Water Saturation ◽  
Resistivity Logs ◽  
Resistivity Inversion ◽  
Induction Logs

Water saturation (Sw) values calculated from resistivity or induction logs are often higher than those measured from core-derived capillary pressure (Pc) measurements. The core-derived Sw measurements are commonly applied for reservoir simulation in preference to the log-derived Sw calculations. As it is economically and logistically impractical to core every hydrocarbon reservoir, a method of correlating the core-derived Sw to resistivity/induction logs is required. Two-dimensional resistivity modelling is applied to dual laterolog data to ascertain the applicability of this technique.The Griffin and Scindian/Chinook Fields, offshore Western Australia, have been producing hydrocarbons since 1994 from two early-to-middle Cretaceous reservoirs, the clean quartzose sandstones of the Zeepaard Formation and the overlying glauconitic, quartzose sandstones of the Birdrong Formation. Routine and special core analysis of cores recovered from wells intersecting these two reservoirs creates an excellent data set with which to correlate the good quality wireline log data.A strong relationship is noted between the modelled water saturation from resistivity logs, and the irreducible water saturation measured from core capillary pressure data. Correlation between the core-derived permeability and the invasion diameter calculated from the modelled laterolog data is shown to produce a locally applicable means of estimating permeability from the resistivity modelling results.The evaluation of these data from the Griffin and Scindian/Chinook Fields provides a method for reducing appraisal and development well analysis costs, through the closer integration of core and wireline log data at an earlier stage of the field appraisal phase.

scholarly journals Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. O1-O19 ◽  
Author(s):  
Mohammad S. Shahraeeni ◽  
Andrew Curtis ◽  
Gabriel Chao
Keyword(s):  
Seismic Data ◽  
Water Saturation ◽  
Random Noise ◽  
Clay Content ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Desktop Computer ◽  
Data Set ◽  
Mixture Density

A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.

Water Mixing During Waterflood Oil Recovery: The Effect of Initial Water Saturation

SPE Journal ◽  
2011 ◽  
Vol 17 (01) ◽  
pp. 43-52 ◽  
Author(s):  
Arne Graue ◽  
Martin A. Fernø ◽  
Robert W. Moe ◽  
Bernard A. Baldwin ◽  
Riley Needham
Keyword(s):  
Oil Recovery ◽  
Water Saturation ◽  
Core Sample ◽  
Initial Water ◽  
Connate Water ◽  
Fluid Saturation ◽  
Secondary Recovery ◽  
Tracer Imaging ◽  
Displacement Processes

Summary This work studies the mixing of injected water and in-situ water during waterfloods and demonstrates that the mixing process is sensitive to the initial water saturation. The results illustrate differences between a waterflooded zone and a preflooded zone during, for example, water-based EOR displacement processes. The mixing of in-situ, or connate, water and injected water during laboratory waterfloods in a strongly water-wet chalk core sample was determined at different initial water saturations. Dynamic 1D fluid-saturation profiles were determined with nuclear-tracer imaging (NTI) during waterfloods, distinguishing between the oil phase, connate water, and injected water. The mixing of connate and injected water during waterfloods, with the presence of an oil phase, resulted in a displacement of all connate water from the core plug. During displacement, connate water banked in front of the injecting water, separating (or partially separating) the injected water from the mobile oil phase. This may impact the ability of chemicals dissolved in the injected water to contact the oil during secondary recovery and EOR processes. The effect of the connate-water-bank separation was sensitive to the initial water saturation (Swi). The time difference between breakthrough of connate water and breakthrough of injected water at the outlet showed a linear correlation to the initial water saturation Swi. The results obtained in chalk confirmed earlier findings in sandpacks (Brown 1957) and thus demonstrated the generality in the results.

Bayesian linearized petrophysical AVO inversion

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M1-M13 ◽  
Author(s):  
Xiaozheng Lang ◽  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Elastic Moduli ◽  
Water Saturation ◽  
Forward Model ◽  
Inversion Method ◽  
Data Set ◽  
Critical Porosity ◽  
Explicit Analytical Solution ◽  
Fluid Properties ◽  
Model Linearization

Seismic reservoir characterization aims to provide a 3D model of rock and fluid properties based on measured seismic data. Petrophysical properties, such as porosity, mineral volumes, and water saturation, are related to elastic properties, such as velocity and impedance, through a rock-physics model. Elastic attributes can be obtained from seismic data through seismic modeling. Estimation of the properties of interest is an inverse problem; however, if the forward model is nonlinear, computationally demanding inversion algorithms should be adopted. We have developed a linearized forward model, based on a convolutional model and a new amplitude variation with offset approximation that combined Gray’s linearization of the reflectivity coefficients with Gassmann’s equation and Nur’s critical porosity model. Physical relations between the saturated elastic moduli and the matrix elastic moduli, fluid bulk modulus, and porosity are almost linear, and the model linearization can be obtained by computing the first-order Taylor series approximation. The inversion method for the estimation of the reservoir properties of interest is then developed in the Bayesian framework. If we assume that the distributions of the prior model and error term are Gaussian, then the explicit analytical solution of the posterior distribution of rock and fluid properties can be analytically derived. Our method has first been validated on synthetic seismic data and then applied to a 2D seismic section extracted from a real data set acquired in the Norwegian Sea.

A Unified Parameter to Represent Reservoir Heterogeneity

2021 ◽  
Author(s):  
Nandana Ramabhadra Agastya
Keyword(s):  
Water Saturation ◽  
Pressure Ratio ◽  
Universal Method ◽  
Permeability Ratio ◽  
Initial Water ◽  
Vertical Permeability ◽  
Radar Chart ◽  
Horizontal Permeability ◽  
Water Cut ◽  
Thief Zone

Abstract We aim to find a universal method and/or parameter to quantify impact of overall heterogeneity on waterflood performance. For this purpose, we combined the Lorenz coefficient, horizontal permeability to vertical permeability ratio, and thief zone permeability to average permeability ratio, with a radar chart. The area of the radar chart serves as a single parameter to rank reservoirs according to heterogeneity, and correlates to waterflood performance. The parameters investigated are vertical and horizontal permeability. Average porosity, initial water saturation, and initial diagonal pressure ratio are kept constant. Computer based experiments are used over the course of this entire research. We conducted permeability studies that demonstrate the effects of thief zones and crossflow. After normalizing these parameters into a number between 0 and 1, we then plot them on a radar chart. A reservoir's overall degree of heterogeneity can be inferred using the radar chart area procedure discussed in this study. In general, our simulations illustrate that the larger the radar chart area, the more heterogenous the reservoir is, which in turn yields higher water cut trends and lower recovery factors. Computer simulations done during this study also show that the higher the Lorenz coefficient, the higher the probability of a thief zone to exist. Simulations done to study crossflow also show certain trends with respect to under tonguing and radar chart area.

DEEP DIELECTRIC-BASED WATER SATURATION IN FRESHWATER AND MIXED SALINITY ENVIRONMENTS

2021 ◽  
Author(s):  
Ping Zhang ◽  
◽  
Wael Abdallah ◽  
Gong Li Wang ◽  
Shouxiang Mark Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Dielectric Permittivity ◽  
Water Saturation ◽  
Low Frequency ◽  
Single Frequency ◽  
Model Simulations ◽  
Petrophysical Parameters ◽  
Effective Medium Model ◽  
Correlation Models ◽  
Resistivity Logs

It is desirable to evaluate the possibility of developing a deeper dielectric permittivity based Sw measurement for various petrophysical applications. The low frequency, (< MHz), resistivity-based method for water saturation (Sw) evaluation is the desired method in the industry due to its deepest depth of investigation (DOI, up to 8 ft). However, the method suffers from higher uncertainty when formation water is very fresh or has mixed salinity. Dielectric permittivity and conductivity dispersion have been used to estimate Sw and salinity. The current dielectric dispersion tools, however, have very shallow DOI due to their high measurement frequency up to GHz, which most likely confines the measurements within the near wellbore mud-filtrate invaded zones. In this study, effective medium-model simulations were conducted to study different electromagnetic (EM) induced-polarization effects and their relationships to rock petrophysical properties. Special attention is placed on the complex conductivity at 2 MHz due to its availability in current logging tools. It is known that the complex dielectric saturation interpretation at the MHz range is quite difficult due to lack of fully understood of physics principles on complex dielectric responses, especially when only single frequency signal is used. Therefore, our study is focused on selected key parameters: water-filled porosity, salinity, and grain shape, and their effects on the modeled formation conductivity and permittivity. To simulate field logs, some of the petrophysical parameters mentioned above are generated randomly within expected ranges. Formation conductivity and permittivity are then calculated using our petrophysical model. The calculated results are then mixed with random noises of 10% to make them more realistic like downhole logs. The synthetic conductivity and permittivity logs are used as inputs in a neural network application to explore possible correlations with water-filled porosity. It is found that while the conductivity and permittivity logs are generated from randomly selected petrophysical parameters, they are highly correlated with water-filled porosity. Furthermore, if new conductivity and permittivity logs are generated with different petrophysical parameters, the correlations defined before can be used to predict water-filled porosity in the new datasets. We also found that for freshwater environments, the conductivity has much lower correlation with water-filled porosity than the one derived from the permittivity. However, the correlations are always improved when both conductivity and permittivity were used. This exercise serves as proof of concept, which opens an opportunity for field data applications. Field logs confirm the findings in the model simulations. Two propagation resistivity logs measured at 2 MHz are processed to calculate formation conductivity and permittivity. Using independently estimated water-filled porosity, a model was trained using a neural network for one of the logs. Excellent correlation between formation conductivity and permittivity and water-filled porosity is observed for the trained model. This neural network- generated model can be used to predict water content from other logs collected from different wells with a coefficient of correlation up to 96%. Best practices are provided on the performance of using conductivity and permittivity to predict water-filled porosity. These include how to effectively train the neural network correlation models, general applications of the trained model for logs from different fields. With the established methodology, deep dielectric-based water saturation in freshwater and mixed salinity environments is obtained for enhanced formation evaluation, well placement, and reservoir saturation monitoring.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Distribution of the Oil Phase Obtained Upon Imbibition of Water

1964 ◽  
Vol 4 (01) ◽  
pp. 49-55 ◽  
Author(s):  
Pietro Raimondi ◽  
Michael A. Torcaso
Keyword(s):  
Primary Production ◽  
Relative Permeability ◽  
Water Saturation ◽  
Initial Conditions ◽  
Flow Behavior ◽  
Phase System ◽  
Residual Oil ◽  
Two Phase ◽  
Connate Water ◽  
Phase Water

Abstract The distribution of the oil phase in Berea sandstone resulting from increasing and decreasing the water saturation by imbibition was investigated Three types of distribution were recognized: trapped, normal and lagging. The amount of oil in each of these distributions was determined as a function of saturation by carrying out a miscible displacement in the oil phase under steady-state conditions of saturation. These conditions were maintained by flowing water and oil simultaneously in given ratios and by using a displacing solvent having essentially the same density and viscosity as the oil.A correlation shows the amount of trapped oil at any saturation to be directly proportional to the conventional residual oil saturation Sir The factor of proportionality is related to the fractional permeability to the water phase. Part of the oil which was not trapped was displaced in a piston- like manner (normal part) and part was eluted gradually (lagging part). The observed phenomena are more than of mere academic importance. Oil which is trapped may well provide the fuel essential for forward combustion and thus be beneficial. On the contrary, in tertiary recovery operations, it is this trapped oil which seems to make current techniques uneconomic. Introduction A typical oilfield may initially contain connate water and oil. After a period of primary production water often enters the field either from surrounding aquifers or from surface injection. During primary production evolution and establishment of a free gas saturation usually occurs. The effect and importance of this third phase is fully recognized. However, this investigation is limited to a two- phase system, one wetting phase (water) and one non-wetting phase (oil). The increase in water content of a water-wet system is termed imbibition. In a relative permeability-saturation diagram such as the one shown in Fig. 1, the initial conditions of the field would he represented by a point below a water saturation of about 35 per cent, i.e., where the imbibition and the drainage curves to the non-wetting phase nearly coincide. When water enters the field the relative permeability to oil decreases along the imbibition curve. At watered-out conditions the relative permeability to the oil becomes zero. At this point a considerable amount of oil, called residual oil, (about 35 per cent in Fig. 1) remains unrecovered. Any attempt to produce this oil will require that its saturation be increased. In Fig. 1 this would mean retracing the imbibition curve upwards. In addition, processes like alcohol and fire flooding, which can be employed at any stage of production, involve the complete displacement of connate water and an increase, or imbibition, of water saturation ahead of the displacing front. Thus, in several types of oil production it is the imbibition-relative permeability curve which rules the flow behavior. For this reason a knowledge of the distribution of the non-wetting phase, as obtained through imbibition, whether "coming down" or "going up" on the imbibition curve, is important. SPEJ P. 49^

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