The present paper describes a technique for determination of the free water level (FWL) in low-permeability chalk reservoirs along slanted or horizontal wells from logged saturations and porosities. The calculation is done by utilizing existing empirical correlations for drainage and imbibition capillary pressure curves. For each logged saturation and corresponding porosity the FWL is determined so that the calculated saturation equals the logged value. The method takes into account possible imbibition, which results in a FWL that is shallower than the original paleo FWL.
The method has been applied to field data from the North Sea and it is shown that it can capture a tilting water level along a horizontal well.
Low-permeability chalk reservoirs are characterized by high capillary entry pressures and large oil/water transition zones. Estimation of oil-in-place for a reservoir at any given time requires information on the reservoir history, a good description of the capillary pressure behavior and knowledge of the FWL. The latter is typically non-horizontal and free water level gradients of more than 100 m/km have been observed in the North Sea. In general the information about the FWL is very scarce, and consists of sometimes dubious pressure measurements. If measured at all, it is usually done in a few exploration wells only. By means of the present method the FWL may be estimated from saturations and porosities logged in production wells.
It is usually assumed that the reservoir is in drainage equilibrium. However, frequently observed residual oil zones indicate that water influx have affected the saturations. (See f. ex. Albrechtsen et al.1). The saturation distribution is a result of drainage as well as imbibition processes and its shape may deviate considerably from the shape of a drainage curve. The present method takes into account possible imbibition, which results in a FWL that is shallower than the original paleo FWL.
Adams 2,3 presented an empirical model, the Imbibition from Drainage or IFD model, relating imbibition capillary pressure curves to the corresponding primary drainage curves. The IFD method was, however, developed for siliciclastic rocks in the Eromanga Basin in Australia and it appears to be less suited for tight chalk with high capillary entry pressures.
A new technique was presented by Bech et al. 4 for the modeling of initial saturations along vertical wells in water wet oil/water reservoirs with large transition zones that are in imbibition equilibrium. The method determines the locations of the existing and the original free water levels and thus the extent of imbibition that the reservoir has undergone by matching calculated and log derived water saturations.
The present paper extends this method to non-vertical wells. The capillary pressure curves including the curves representing the scanning between the drainage and imbibition curves are described by analytical expressions as shown in Skjaeveland et al.5. Optionally, the scanning may be described by Killoughs method 6. The drainage capillary pressure shape function, the irreducible water saturation and the capillary entry pressure are modeled by the EQR method 7. The method is demonstrated on a synthetic example and applied to field data from the North Sea and it is shown that it can capture a tilting water level along a horizontal well.
Determination of Present Free Water Level
The FWL is determined for each saturation and corresponding porosity logged along the well. It is assumed that the oil accumulation was originally formed through a pure drainage process resulting in a drainage equilibrium saturation distribution and a palaeo free water level. In case that subsequent burial and/or tectonic events have lead to an influx of water, the free water level has risen and a new saturation distribution has emerged in the reservoir. This new saturation distribution is a result of both drainage and imbibition processes and can therefore not be described by a model which assumes drainage equilibrium. The changes in water saturation and FWL is illustrated in Fig. 1 for a constant-porosity vertical well. It is seen that the oil-water contact is unaltered and that a region with residual oil has been left below the rising water table.
This paper describes the implementation of a petroelastic model (PEM) based on Gassmann's equation to calculate seismic attributes into a commercial reservoir flow simulator. This implementation is the first step of a project to integrate time-lapse (4D) seismic attributes into an assisted history matching tool developed in a previous project.
The paper includes the description of the PEM and some implementation issues, such as the coupling of the model with the flow simulator with the purpose of using its basic calculated properties, discuss some user options (such as properties input through correlation or geoestatisticaly obtained maps) and the model variants and extensions (such as lithology influence and pressure effects). Three applications of this petroelastic model are shown: the first is a synthetic model based on outcrop data; the second is a 4D feasibility study for water injection monitoring in an offshore field; and the last one is a comparison between observed and calculated pressure impedances for an offshore field.
The resulting tool is applicable, for example, in 4D seismic feasibility studies, in seismic modeling for comparison with observed surveys and makes possible further implementations for incorporating the seismic data in assisted history matching.
The use of petroelastic attributes has several useful purposes1, such as feasibility of applying 4D seismic monitoring, optimize 4D seismic monitoring program and prepare more accurate production forecasts.
A possible workflow for applying 4D seismic in the monitoring of fluid flow in porous media follows the iterative steps2:
Steps 3 and 4 are unnecessarily cumbersome because most flow simulators do not calculate reservoir seismic attributes. As a result, information from the flow simulation Step 3 must be converted to a format suitable for analysis in the PEM Step 4.
In addition, errors may be introduced into the calculation of seismic attributes if fluid properties in the PEM do not match the corresponding fluid properties in the flow simulator, like using standard correlations of fluid properties.
These problems can be avoided if the PEM is incorporated into the flow simulator, eliminating the need of a third-party software to calculate the seismic attributes, so that it uses exactly the same fluid property model.
Fanchi1 shows the results for some reservoir management scenarios, applying successfully the petroelastic properties information calculated through an integrated flow simulator using the Gassmann's equation3, improving the reservoir management and monitoring processes.
Gosselin et al.4 also implemented an integrated flow simulator tool5 using the Gassmann's equation in a project to integrate 4D data into an assisted history matching process.
The ultimate aim of this project is to incorporate time-lapse seismic attributes into an assisted history match (AHM) tool, which combines efficient derivative calculation and robust optimization techniques, already developed in a previous project6 through an integrated reservoir flow simulator, facilitating a lot the viable use of this kind of data.
Gomez, Hugo (C.A.P.S.A. Capex) | Stinco, Luis P. (CAPEX SA) | Nawratil, Alejandro Enrique (CAPSA-CAPEX) | Lopetrone, Javier (Baker Atlas) | Romero, Pedro Antonio (Baker Atlas) | Saavedra, Benito Eduardo
In the Golfo San Jorge basin, Argentina, the Timur-Coates permeability index obtained from the T2 distributions is considered as a good indicator of reservoir quality and has been used as a very important variable for production forecast. However, when heavy oil is present, having a relaxation time below the standard free fluid-bound fluid cut-off of 33 ms, it is conventionally counted as part of the bound fluid, independently of its mobility. For this reason, in case of
movable heavy oil, the standard Timur-Coates permeability index using 33 ms tends to be always pessimistic, in eventual disagreement with other reservoir quality indicators as the SP curve, depositional environment, cuttings and production data.
In order to perform a correction of the permeability index for the presence of heavy oil, two layers of one well from the Diadema field, with a complete set of SP, Resistivity, MREX and production data was selected and evaluated using 2DNMR (T2intrinsic - Diffusion) maps, which uses the diffusivity contrast for discriminating between capillary bound water and heavy oil, within the bound fluid window (BVI). The clay bound water (CBW) cut off has been chosen to be 6 ms.
The results show that the corrected Timur-Coates permeability can increase by an order of magnitude in the tested zone of the reservoir layers, but can become even higher within the whole layers, which is a reasonable estimation for the corresponding channel depositional environment. The production data also support the interpretation, indicating that the NMR rock quality estimation can be performed more accurately even in the presence of heavy oil. The corrected Timur-Coates permeability values can be used in a future update for forecasting well production.
The Golfo de San Jorge basin, Argentina, located in the heart of Patagonia, and extending from the Atlantic Ocean to the Andean foothills, the San Jorge basin accounts for around one third of the hydrocarbon production in Argentina (Fig. 1). The origin and subsequent geological evolution of the basin are caused by the rift process responsible for the opening of the Atlantic Ocean in early Jurassic times. Accumulation of terigeneous sediments continued well into early
Cretaceous times1. Clastic deposition in the hydrocarbon-producing zone is characterized by thick shale laminations of lacustrine and flood-plain origin, interspersed with much thinner and laterally sparse sand units that today serve as hydrocarbon reservoirs. The relatively small concentration of sand units in the sedimentary column is explained by their ephemeral fluvial origin, which could only account for their effective clastic accumulations between 0.5 and 15 m (1.6 to 49.2 ft), but predominantly thinner than 4 m (13.1 ft). Starting in early Cretaceous times, Andean tectonic activity
caused yet another significant perturbation of sedimentary column in the form of finely laminated deposits of tuffs of pyroclastic origin associated with intermittent pulses of volcanic eruptions. The presence of tuffs has altered significantly the original petrophysical properties of existing sand units. Subsequent structural deformation adversely modified the already marginal porosity and permeability of the sands and caused extensive fracture damage in the existing tuff units.
This paper analyzes the effect of stress on the rock properties fracture and matrix compressibilities, fracture and matrix porosities, and permeability in naturally fractured reservoirs (NFRs).
In NFRs, fluids are stored inside the matrix pore space and inside the fractures of the rock. The reservoir characterization parameter indicating the volumetric fraction of fluids deposited inside the fractures is the storage capacity ratio, which is function of the fracture and matrix porosity, and fracture and matrix total compressibilities. Due to the difficulty to obtain these values, in reservoir engineering computations such as pressure transient analysis and reservoir simulation, among others, it is generally assumed that the matrix and the fracture total compressibilities are equal. This induces a big uncertainty in the estimation of the storage capacity ratio and leads to a wrong estimation of the volume of fluids inside the fractured rock.
Changes in pore pressure due to production or injection of fluids affect the effective reservoir in-situ stress. The mechanical behavior of the fractured rock and its effects on the rock properties permeability, porosity and compressibility in the matrix and fracture frames are analyzed using the elastic properties bulk modulus and normal compliance of the fracture. These properties can be obtained from petrophysical core analysis or multi-component seismic interpretation, and linked to pressure transient analysis through the storage capacity ratio equation. A step-by-step procedure of the analysis is presented and illustrated with an example for quantifying the effects of changes in effective stress on the fracture and matrix compressibilities.
Well test analysis has been one of the most basic tools to characterize and quantify properties such as permeability, storage capacity ratio and the interporosity flow parameter in naturally fractured reservoirs. This reservoir characterization study integrates several geosciences such as: Petrophysics, Rock Mechanics, Seismic, Geophysics, Reservoir Engineering, and Production Engineering to investigate and quantify the effect of stress on several rock properties: permeability, compressibility, and porosity of naturally fractured reservoirs.
Dual Porosity Models
These models are used to simulate reservoir systems composed of two different types of porosity, matrix and fracture that coexist in a rock volume. It is usually assumed that the matrix consists of a set of porous rock systems that are not connected to each other, have a low transmissibility and have a high storage capacity. Furthermore, it is also assumed that the fracture system has low storage capacity, high transmissibility and it interconnects the porous media.
Most dual porosity models assume that the matrix provides the fluids to the fractures, and the fractures transport the fluids to the well. As shown in Figure 1, different idealizations of the matrix/fracture geometry have been proposed such as the sugar cube model by Warren and Root1, parallel horizontal fractures by Kazemi2 and match-stick column models by Reiss3. The multi-porosity model proposed by Abdassh and Ershaghi4 is a variation of the dual porosity model, which assumes a fracture set that interacts with two groups of matrix blocks with different porosities and permeabilities.
The use of seismic attributes has increased, especially when extracted from interpreted horizons. The various available attributes are not independent from each other but represent, in fact, different ways of presenting and studying fundamental information from seismic data (time, amplitude, frequency and attenuation). However, statistical analysis using attributes must be based on geological knowledge and not only on mathematical correlation. Petrophysical studies and seismic modeling are sources of understanding. Such knowledge is necessary to improve confidence in observed correlations with reservoir parameters and must be part of all attribute analysis.
However, the use of seismic attributes leads to several questions, for example, what do they all mean? When to use one or another? How to use them on geologic modeling? How reliable those data are? The answers to these questions are not easy, but considering about petrophysical modeling (Porosity, NTG and permeability) what is the best approach: to consider only well data, that are punctual and need to be interpolated, or try to find correlation with physical measurements (seismic data)? Not to consider seismic attributes makes one feel coming back in time, when this important tool was not available.
On a giant oilfield offshore Brazil seismic attributes (‘conventional', complex trace, polynomial decomposition, geometric and coherence) have been used to create geological models and to reduce uncertainties. The attribute choice must be performed by the geophysicist and the geologist working together, in order to check geological meaning of attribute maps, possible physical meaning of the attribute, etc. Plots of the highest correlation values should be visually inspected in order to choose the attribute with best correlation to the desired parameters.
The results show attributes have been favourable to porosity and NTG prediction, but regular (at maximum) to permeability. For permeability even if the results are not so good, the correlation are improving for the latest models (as long as new wells are used). Polynomial decomposion and complex trace attributes have shown better results.
Introduction: seismic attribute definitions and discussions
The use of seismic attribute data for prediction of detailed reservoir properties began more than 30 years ago.
In fact, a seismic attribute is any property derived from seismic reflection signal. Attributes may be compared to lithology in an attempt to devise a method of property prediction away from well control. The method of prediction can vary from a simple linear correlation to multi-attribute analysis, geostatistical methods, etc.
As an evidence of current proliferation the use of attributes, Chen and Sidney (1997) have catalogued more than 60 commom seismic attributes along with a description of their apparent significance and utility.
Although there is a rich history of seismic attributes use in reservoir prediction, the practice remains a difficult and uncertain task. The bulk of this uncertainty arises from the nature of the physics connecting a number of attributes to a corresponding reservoir property. Due to the complex and varied physical processes responsible for various attributes the unambiguous use of attributes for direct prediction will probably remain a challenge for the years to come.
In addition to the fact above described, there is the possibility of coming across statistical pitfalls while using multiple attributes for empirical reservoir property prediction. In addition, many attributes are derived using similar signal processing methods and can, in some cases, be considered largely redundant with respect to their description of the seismic signal.
Monroy-Ayala, Norberto (Pemex E&P) | Munoz, Rogelio (Pemex) | Rico, Raul (Halliburton) | Palacios, Cesar (Halliburton Energy Services Group) | Torne, Juan Pablo (Halliburton Energy Services) | Leuro, Jorge | Fam, Maged Y.
Petrophysical evaluation of the tight gas reservoirs in northern Mexico presents challenges for identifying overpressured gas zones when applying conventional techniques in laminated and fine grain sand zones. This paper presents case histories in which the magnetic resonance technology was used in its new T1 mode and describes the advantages of this new logging mode over the conventional T2 mode in gas reservoirs. The paper highlights the integration of all available petrophysical data for a particular field. This integration model compares the irreducible water saturation between the newly developed models, based on magnetic resonance data after its calibration to core, conventional logs, and production tests. Our integrated approach was used to analyze the data from different wells drilled in the field and to determine the best petrophysical parameters for any interpretation. The approach used was focused on identifying the best zones to be further evaluated, considering the different responses of all available log data from each well in the field.
Permeability was derived from the Coates model using the magnetic resonance data after its calibration to cores. Results were then compared to actual production. For those wells in which only standard MRI logging information was available, the model was used to calculate the permeability and compare it to actual cores. Production test results were also compared to the evaluation prognosis and used to fine tune the interpretation model. The model was then used in other wells in the area for which few conventional log data were acquired.
The integrated approach used with the new MRI acquisition technique benefits the operators by helping them to better evaluate and produce laminated tight gas reservoirs. The methods developed helps with making the right decisions regarding the need to acquire additional log data, with defining intervals for testing, and determining the size of the required hydraulic fracturing for production.
In overpressured tight gas reservoirs, permeability is one of the critical parameters used to characterize the reservoir and to determine the best fracture design for maximizing its gas recovery.
One of the options for obtaining reliable reservoir permeability is to cut cores, using conventional methods or by using wireline rotary coring techniques, and send these rock samples to a petrophysical laboratory for permeability measurements. This is usually a single point measurement.
Conversely, because permeability ranges only from 0.001 md to 0.1 md in this tight gas field, pressure tests or build-up analyses have limitations. The use of magnetic resonance to determine the irreducible water saturation and permeability using the Coates relationship (Coates, et al., 1999) is proven to be a reliable option in this type of reservoir. This relationship relates the total volume of fluids to the nonmoveable irreducible fluids in the pore space to determine the permeability. It has given good results, especially in sandstones.
The original Coates relationship is expressed as follows:
K = Permeability
f = MRIL Porosity
MFFI = MRI Free Fluid Volume
MBVI = MRI Bulk Volume Irreducible
C = Coefficient dependent on reservoir type
a, b= Exponents derived from NMR core analysis
Typical values for the exponents in sandstones in the Gulf of Mexico are C=20 and a, b= 2
In general, the MRI permeability should initially be used to differentiate between good quality and poor quality reservoirs in a relative fashion. After calibrating to the MRI core analysis and defining the values of "C,?? "a,?? and "b,?? it can be used in its absolute form. It should be closely compared to the Klinkenberg permeability (air permeability corrected for overburden) measured in the laboratory (Marschall, et al., 1999).
The Beta distribution in n-dimensions is introduced to describe the proportions of the mineralogical components existing in a certain stratigraphic interval (the porosity is included as a "mineralogical component??). The justification for doing so is empirical. The model allows the calculation of well logging parameters, such as GRma, GRsh, shale density, etc., without having to introduce them by "eye??. It also allows the probabilistic calculation of the rock composition at each depth when there are more mineralogical components than logs: that is, there is a shortage of equations. In addition to this, the Beta model can be used to test the hypothesis that the relationship between any two components can be regarded as random, which should have applications in reservoir characterization.
This work is the result of the authors' several experiences in sandy reservoirs of clay minerals matrix in the San Jorge Basin. It is intended to highlight the advantages of the use of integrated reservoir models originated from the group of ordinary lithologic characteristics among reservoirs, their integration to sedimentary subambients inferred from logs, and high technology log data (spectral gamma ray and magnetic resonance).
The majority of the reservoirs of the Bajo Barreal formation are volcanoclastic sandstones with porosities modified and/or reduced with burial and as diagenesis grades increase. That decrease mainly takes place due to three processes: mechanic compaction, dissolution of grains by intergranular contacts, and pore cementation.
The quantization of the compaction is influenced by the abundance and type of lithic material. The use of technologies that make it possible to have a detailed estimate of the lithology (Spectral Gamma Rays, Lateral Impact and Rotated Cores) is of pre-eminent importance when evaluating that type of reservoirs.
The diagenetic conditions and processes directly affect porosity determination from conventional logs. This justifies the use of porosity tools that are independent from the type of material of the reservoir rock. However, the combination of porosity data obtained in the NMR together with lithologic determinations and appropriate logs results into an interesting alternative to improve the evaluation of sandy reservoirs.
Generally, it is considered that volcanoclastic sandstones have a poor potential as oil reservoirs, because of their low porosity and permeability due to compactational processes and precipitation of authigenic mineral, such as cement. But thanks to the high reactivity of their materials with fluids from the reservoir, secondary important porosities are developed making the reservoir a high quality one. For this reason, these processes can be used to indicate the quality of volcanoclastic sandstones.
The implementation of methodologies, as the ones in this work, endeavors to apply evaluation criteria of sandy reservoirs.
Artificial neural networks are becoming increasingly popular in the oil and gas industry. In the past, studies have been done on the use of artificial neural networks in reservoir characterization, field development and formation damage prediction, to name a few. The aim of this study is to provide guidelines to successfully develop and train an artificial neural network (ANN) that will predict reservoir properties that can give an improved history match when input into a reservoir simulation model. An ANN was developed to improve the history match with a ‘small' number of simulation runs for a reservoir that produced oil, gas and water for a period of ten years. Due to a lack of specific protocols for this type of study, the trial and error process was utilized to establish guidelines and suggestions.
The neural network was developed by using an inverse solution method to formulate the training and testing data. Normalization of the data simplified the neural network, improved its effectiveness and enhanced its performance. The feed-forward network with back-propagation and the hyperbolic tangent sigmoid function (tansig) in the hidden layers of the network proved to be most effective in the training/learning process.
Results indicated that functional links and eigenvalues of various system related matrices were effective in the training/learning process. These provided the network with the necessary connections that linked the inputs to the required outputs. It was necessary to input production differences between the historical and simulated performances at specific times to successfully train the network and predict realistic property values for the reservoir. Data structure and production time intervals influenced the training time as well as the accuracy of the predictions. If time intervals were too short, training times were longer, memorization occurred, and the network could not accurately predict the reservoir's properties. Most of the effective functional links that were successful in the training/learning process included relationships between permeability and other factors such as porosity, areas of the regions in the reservoir and the distances from the producer to the boundaries of the reservoir.
The M4.1 reservoir in the Tahoe Field located in the Gulf of Mexico was used as a case study to illustrate the use of ANNs in decreasing the amount of numerical reservoir simulations required to obtain an improved history match. The effective parameters, obtained from network development, were applied to data from the M4.1 reservoir simulations to determine which functional links and architecture would be most effective in training the network. It was observed that some of the functional links and network structures that were effective in network development were also effective in the ANN developed for the M4.1 reservoir while some were not.
Artificial neural networks are information processing systems that are a rough approximation and simplified simulation of the biological neuron network system. The first practical application of ANNs came in the late 1950s when Frank Rosenblatt and his colleagues demonstrated their ability to perform pattern recognition1. However, interest in neural networks dwindled due to its limitations as well as the lack of new ideas and powerful computers1. With some of these hurdles overcome in the 1980s, and with the development of the back-propagation algorithm for training multilayer perceptron networks, there was a renewed interest in the field. Since then, ANNs have been improved and applied in aerospace, automotive, defense, transportation, tele-communications, electronics, entertainment, manufacturing, financial, medical and the oil and gas industry, to name a few.
In recent years, there has been a growing interest in applying ANNs to petroleum engineering. ANNs in the oil and gas industry are based on supervised training algorithms that have the potential for solving many of the challenging and complex problems in the oil and gas industry2. Previously, some of the studies done on the applications of neural networks have been in reservoir characterization, field development, two-phase flow in pipes, and identification of well test interpretation models, completion analysis, formation damage prediction, permeability prediction and fractured reservoirs.
A rapid and an effective reservoir simulation model was built based on limited information. 3D seismic impedance, two exploratory wells' log interpretation, a core data, and a well test consisting of an isochronal test and, several months later, an extended flow followed by long build up test were basic available data for this field. The main objective of this study is to estimate recoverable reserve with or without hydraulic fracturing in Mulichinco formation at the Paso Del Indio Field.
Material balance and decline curve analyses have important limitations to estimate ultimate recoverable reserves in the tight gas reservoirs. Well types that are derived from the conceptual simulation models do not reflect the effective drainage area or permeability heterogeneity in the field. A representative permeability in well spacing area should be averaged harmonically or geometrically. In order to estimate the ultimate recoverable reserves in the tight gas reservoirs, permeability heterogeneity or the effective available drainage area to hydraulic fractures should be simulated effectively. Relatively small changes in permeability can results in unsuccessful fracture design and in uneconomical flow rates.
A very quick and effective simple reservoir simulation model was established to estimate recoverable reserves rather than using conventional volumetric, material balance, and decline curve analysis in tight gas reservoirs. Not having any production histories, well test information was used very successfully as history matching information to validate the geological, petrophysical, and PVT models.