Artificial-Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs

2009 ◽  
Vol 12 (01) ◽  
pp. 96-103 ◽  
Author(s):  
Saud M. Al-Fattah ◽  
Hamad A. Al-Naim
Keyword(s):  
Experimental Data ◽  
Relative Permeability ◽  
Reservoir Simulation ◽  
Nonlinear Problems ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Porous Rocks ◽  
Data Set ◽  
Ann Models ◽  
Permeability Data

Summary Determination of relative permeability data is required for almost all calculations of fluid flow in petroleum reservoirs. Water/oil relative permeability data play important roles in characterizing the simultaneous two-phase flow in porous rocks and predicting the performance of immiscible displacement processes in oil reservoirs. They are used, among other applications, for determining fluid distributions and residual saturations, predicting future reservoir performance, and estimating ultimate recovery. Undoubtedly, these data are considered probably the most valuable information required in reservoir simulation studies. Estimates of relative permeability are generally obtained from laboratory experiments with reservoir core samples. In the absence of the laboratory measurement of relative permeability data, developing empirical correlations for obtaining accurate estimates of relative permeability data showed limited success, and proved difficult, especially for carbonate reservoir rocks. Artificial-neural-network (ANN) technology has proved successful and useful in solving complex structured and nonlinear problems. This paper presents a new modeling technology to predict accurately water/oil relative permeability using ANN. The ANN models of relative permeability were developed using experimental data from waterflood-core-tests samples collected from carbonate reservoirs of giant Saudi Arabian oil fields. Three groups of data sets were used for training, verification, and testing the ANN models. Analysis of results of the testing data set show excellent agreement with the experimental data of relative permeability. In addition, error analyses show that the ANN models developed in this study outperform all published correlations. The benefits of this work include meeting the increased demand for conducting special core analysis (SCAL), optimizing the number of laboratory measurements, integrating into reservoir simulation and reservoir management studies, and providing significant cost savings on extensive lab work and substantial required time.

Application of Machine Learning to Interpret Steady State Drainage Relative Permeability Experiments

2021 ◽  
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Steady State ◽  
Relative Permeability ◽  
Learning Model ◽  
Gradient Boosting ◽  
Data Set ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting

Abstract A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results. The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Reservoir Simulation With History-Dependent Saturation Functions

1976 ◽  
Vol 16 (01) ◽  
pp. 37-48 ◽  
Author(s):  
J.E. Killough
Keyword(s):  
Experimental Data ◽  
Relative Permeability ◽  
Reservoir Simulation ◽  
Correct Prediction ◽  
Hysteresis Model ◽  
Relative Permeabilities ◽  
Capillary Hysteresis ◽  
Scanning Curves ◽  
Capillary Pressures ◽  
Smooth Transitions

Abstract A history-dependent model for saturation functions, combined with a three-dimensional, three-phase, semi-implicit reservoir simulator, has been developed. In water-coning simulations with variable rates, for waterflooding in the presence of free gas saturations, and for gas-cap shrinkage, use of hysteresis in saturation functions shows results significantly different from those obtained by conventional methods. To some extent, the model is based upon remembering the saturation history of the reservoir. In doing this, smooth transitions of both relative permeabilities and capillary-pressures from permeabilities and capillary-pressures from drainage-to-imbibition or imbibition-to-drainage states are allowed. In addition, the effect of trapped gas or oil saturations on relative permeabilities and capillary pressures is accounted for. Tests of the model indicate that simulation with hysteresis is a stable-procedure requiring little more computation time and storage than normal simulations. In addition, results of these tests agree qualitatively with experimental and field results. Introduction Present-day reservoir simulators have allowed investigation of complex recovery schemes and production schedules. Although simulators can production schedules. Although simulators can handle such problems numerically, most treat saturation functions in a simplified manner. For example, only one set of saturation functions may be used for initialization and/or simulation in a particular part of the reservoir. It is assumed that particular part of the reservoir. It is assumed that saturation changes occur in a given direction - drainage or imbibition-for most of the simulation. Cutler and Rees pointed out that hysteresis in capillary pressures may affect well coning behavior. Other authors have shown that hysteresis in relative permeabilities is important in the correct prediction of reservoir behavior. Unless treated prediction of reservoir behavior. Unless treated more realistically, the history dependence of saturation functions could cause significant errors in reservoir simulation. This paper describes a reservoir simulation technique in which saturation-function hysteresis is accounted for. A model for hysteresis is incorporated, permitting smooth transitions in either direction between drainage and imbibition relative permeability and capillary pressure curves as observed in experimental data. Including this hysteresis model allows the simulator to predict more realistically many reservoir situations. THE HYSTERESIS MODEL The hysteresis model allows both capillary pressures and relative permeabilities to range pressures and relative permeabilities to range between imbibition and drainage curves via intermediate "scanning" curves. Experimental data are required only for the bounding imbibition and drainage functions since the model provides an interpolative scheme for arriving at the intermediate values. However, regression parameters are incorporated allow a closer fit with experimental scanning states, should these data exist. The model also allows the use of analytical curves for the bounding relative-permeability functions, for which data may not exist. The hysteresis model has been designed so that saturation functions derived from the hysteresis algorithm approach physical reality. To this extent, the existing experimental data have been used as the basis for the model. The following sections describe these data and the associated procedures for calculating hysteretic relative permeabilities and capillary pressures. Further details and equations are given in the Appendix. CAPILLARY HYSTERESIS Capillary hysteresis is characterized by bounding imbibition and drainage curves and intermediate scanning curves, as shown in Fig. 1. SPEJ P. 37

INVESTIGATION OF LOW SALINE WATER’S EFFECTS ON RELATIVE PERMEABILITY IN CARBONATE RESERVOIRS

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Hamed Hematpour ◽  
Mohammad Parvazdavani ◽  
Saeed Abbasi ◽  
Syed Mohammad Mahmood
Keyword(s):  
Relative Permeability ◽  
Low Cost ◽  
Point Of View ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Water Flooding ◽  
Karoon River ◽  
The North ◽  
Relative Permeability Curves ◽  
The Persian Gulf

Low Salinity Water flooding (LSW) is one of the favorable subsets of water flooding EOR methods due to its great advantages over normal water flooding; having a low cost of operation and being environmentally-friendly. LSW has been studied in mathematical, experimentally and practically point of view in numerous numbers of sandstone cases in the worldwide.  Existing of giant carbonate reservoirs containing a great amount of petroleum in the regions of the North Sea and the Middle East have been turned into a motivation for the relevant experts to focuses on the possibility of running an LSW project in a carbonate reservoir. Accordingly, this paper aims to investigate this possibility through running two sets of flooding tests on selected cores from one of Iranian carbonate reservoirs. In more details, on each core two water flooding tests have been conducted in which the first test have been run by a sample of water from the Persian Gulf with high salinity and in the second one the injected water has been from Karoon River with a lower rate of salinity. Then, the recovery factor from both tests of a target core has been compared. The results indicate that running an LSW have been caused improvement in recovery factors which was approved by relative permeability curves analysis.

Petrofacies classification using machine learning algorithms

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA101-WA113 ◽  
Author(s):  
Adrielle A. Silva ◽  
Mônica W. Tavares ◽  
Abel Carrasquilla ◽  
Roseane Misságia ◽  
Marco Ceia
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Water Saturation ◽  
Carbonate Reservoir ◽  
Machine Learning Algorithms ◽  
Carbonate Reservoirs ◽  
Training Data ◽  
Southeastern Brazil ◽  
Gradient Boosting ◽  
Data Set

Carbonate reservoirs represent a large portion of the world’s oil and gas reserves, exhibiting specific characteristics that pose complex challenges to the reservoirs’ characterization, production, and management. Therefore, the evaluation of the relationships between the key parameters, such as porosity, permeability, water saturation, and pore size distribution, is a complex task considering only well-log data, due to the geologic heterogeneity. Hence, the petrophysical parameters are the key to assess the original composition and postsedimentological aspects of the carbonate reservoirs. The concept of reservoir petrofacies was proposed as a tool for the characterization and prediction of the reservoir quality as it combines primary textural analysis with laboratory measurements of porosity, permeability, capillary pressure, photomicrograph descriptions, and other techniques, which contributes to understanding the postdiagenetic events. We have adopted a workflow to petrofacies classification of a carbonate reservoir from the Campos Basin in southeastern Brazil, using the following machine learning methods: decision tree, random forest, gradient boosting, K-nearest neighbors, and naïve Bayes. The data set comprised 1477 wireline data from two wells (A3 and A10) that had petrofacies classes already assigned based on core descriptions. It was divided into two subsets, one for training and one for testing the capability of the trained models to assign petrofacies. The supervised-learning models have used labeled training data to learn the relationships between the input measurements and the petrofacies to be assigned. Additionally, we have developed a comparison of the models’ performance using the testing set according to accuracy, precision, recall, and F1-score evaluation metrics. Our approach has proved to be a valuable ally in petrofacies classification, especially for analyzing a well-logging database with no prior petrophysical information.

Electromagnetic dispersion and sensitivity characteristics of carbonate reservoirs

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. E377-E388
Author(s):  
Yan Gao ◽  
Jinsong Shen ◽  
Zhanxiang He ◽  
Chao Ma
Keyword(s):  
Experimental Data ◽  
Effective Means ◽  
Excitation Frequency ◽  
Sensitivity Study ◽  
Carbonate Reservoirs ◽  
Dispersion Effect ◽  
Frequency Range ◽  
Data Set ◽  
Oil Saturation ◽  
Carbonate Formation

The need to develop a new measurement method for the identification of favorable hydrocarbon-producing zones in carbonate reservoirs is of great importance due to their complex pore structure and lithology. We have evaluated fundamental experimental data acquired from carbonate formation samples in a buried-hill reservoir to demonstrate the magnitude of the complex resistivity (CR) dispersion effect in oil-saturated carbonate rocks and the possibility of identifying hydrocarbon-bearing carbonate sections. Our experimental data set included CR measurements on four carbonate core samples with various degrees of oil saturation, and at each saturation level, CR measurements were acquired in a frequency range between 10 Hz and 100 kHz. The experimental data indicated that the intensity of the CR dispersion effect increased with oil saturation above a specific critical excitation frequency. The experimental spectra of the carbonate samples were fitted with the Debye model and the Cole-Cole model (CCM), which can be used to interpret the dispersion effect over a wide frequency range. Based on the experimental data and the inverted CCM parameters, a sensitivity study was carried out with the crosswell frequency-domain electromagnetic (EM) modeling. Multifrequency EM data were acquired directly by calculating the Maxwell equation with the CR. Our experimental data and forward modeling results indicated that measurement of induced polarization and EM coupling effects can be an effective means for identifying carbonate hydrocarbon reservoirs and for quantitatively evaluating the effect of formation saturation on favorable conditions.

scholarly journals Correlation between Shear Wave Velocity and Porosity in Porous Solids and Rocks

2013 ◽  
Vol 2013 ◽  
pp. 1-3
Author(s):  
J. Kováčik ◽  
Š. Emmer
Keyword(s):  
Experimental Data ◽  
Shear Modulus ◽  
Shear Wave ◽  
Wave Velocity ◽  
Power Law ◽  
Shear Wave Velocity ◽  
Porous Solids ◽  
Porous Rocks ◽  
Data Set ◽  
Porosity Dependence

The shear wave velocity dependence on porosity was modelled using percolation theory model for the shear modulus porosity dependence. The obtained model is not a power law dependence (no simple scaling with porosity), but a more complex equation. Control parameters of this equation are shear wave velocity of bulk solid, percolation threshold of the material and the characteristic power law exponent for shear modulus porosity dependence. This model is suitable for all porous materials, mortars and porous rocks filled with liquid or gas. In the case of pores filled with gas the model can be further simplified: The term for the ratio of the gas density to the density of solid material can be omitted in the denominator (the ratio is usually in the range of (10−4, 10−3) for all solids). This simplified equation was then tested on the experimental data set for porous ZnO filled with air. Due to lack of reasonable data the scientists are encouraged to test the validity of proposed model using their experimental data.

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