Probabilistic Dynamic Modelling and Prediction Workflows: Application to Multi-Layered Waterflood Reservoir with 90 Years Production History and 293 Wells

2021 ◽  
Author(s):  
Ecko Noviyanto ◽  
Deded Abdul Rohman ◽  
Theoza Nopranda ◽  
Rudini Simanjorang ◽  
Kosdar Gideon Haro ◽  
...  
Keyword(s):  
Experimental Design ◽  
History Matching ◽  
Pearson Correlation ◽  
Probabilistic Method ◽  
Dynamic Modelling ◽  
Development Planning ◽  
Base Case ◽  
Objective Functions ◽  
Parameter Uncertainties ◽  
Pareto Chart

Abstract This paper presents a probabilistic modeling and prediction workflow to capture the range of uncertainties and its application in a field with many wells and long history. A static model consisting of 19 layers and 293 wells was imported as the base model. Several reservoir properties such as relative permeability, PVT, aquifer, and initial condition were analyzed to obtain the range of uncertainties. The probabilistic history matching was done using Assisted History Matching (AHM) tools and divided into experimental design and optimization. The inputted parameters and their range sensitive to objective functions, e.g., oil rate/total difference, could be determined using a Pareto chart based on Pearson Correlation during experimental design. The optimization phase carried over the most sensitive parameters and utilized Particle Swarm Optimization (PSO) algorithm to iterate the process and find the equiprobable models with minimum objective functions. After filtering a set of models created by AHM tools by the total oil production, field/well oil objective functions, the last three years' performance, and clustering using the k-means algorithm, there are 11 models left. These models were then analyzed to understand the absolute risk and parameter uncertainties, e.g., mobile oil or sweep efficiency. Three models representing P10, P50, and P90 were picked and used as the base models for developing waterflood scenario designs. Several scenarios were done, such as base case, perfect pattern case, and existing well case. The oil incremental is in the range of 1.60 – 2.01 MMSTB for the Base Case, 7.57 – 9.14 MMSTB for the Perfect Pattern Case, and 6.01 – 7.75 MMSTB for the Existing Well Case. This paper introduces the application of the probabilistic method for history matching and prediction. This method can engage the uncertainty of the dynamic model on the forecasted production profiles. In the end, this information could improve the quality of management decision-making in field development planning.

Capturing the Reservoir Uncertainty through Probabilistic Dynamic Modelling and Prediction: A Case Study of Multi-Layered Waterflood Reservoir with 90 Years Production History and 293 Wells

2021 ◽  
Author(s):  
E. Noviyanto
Keyword(s):  
Experimental Design ◽  
History Matching ◽  
Pearson Correlation ◽  
Probabilistic Method ◽  
Dynamic Modelling ◽  
Development Planning ◽  
Base Case ◽  
Objective Functions ◽  
Parameter Uncertainties ◽  
Pareto Chart

This paper presents a probabilistic modeling and prediction workflow to capture the range of uncertainties and its application in a field with many wells and long history. A static model consisting of 19 layers and 293 wells was imported as the base model. Several reservoir properties such as relative permeability, PVT, aquifer, and initial condition were analyzed to obtain the range of uncertainties. The probabilistic history matching was done using Assisted History Matching (AHM) tools and divided into experimental design and optimization phases. The inputted parameters and their range sensitive to objective functions, e.g., oil rate/total difference, could be determined using a Pareto chart based on Pearson Correlation during experimental design. The optimization phase carried over the most sensitive parameters. It utilized Particle Swarm Optimization (PSO) algorithm to iterate the process and find the equiprobable models with minimum objective functions. After filtering a set of models created by AHM tools by the total oil production, field/well oil objective functions, the last three years' performance, and clustering using the k-means algorithm, there are 11 models left. These models were then analyzed to understand the final risk and parameter uncertainties, e.g., mobile oil or sweep efficiency. Three models representing P10, P50, and P90 were picked and used as the base models for developing waterflood scenario designs. Several scenarios were done, such as base case, perfect pattern case, and existing well case. The oil incremental is in the range of 1.60 – 2.01 MMSTB for the Base Case, 7.57 – 9.14 MMSTB for the Perfect Pattern Case, and 6.01 – 7.75 MMSTB for the Existing Well Case. This paper introduces the application of the probabilistic method for history matching and prediction. This method can engage the uncertainty of the dynamic model on the forecasted production profiles. In the end, this information could improve the quality of management decision-making in field development planning.

Semiautomatic Multiple Resolution Design for History Matching

SPE Journal ◽  
2007 ◽  
Vol 12 (04) ◽  
pp. 408-419 ◽  
Author(s):  
Baoyan Li ◽  
Francois Friedmann
Keyword(s):  
Fluid Flow ◽  
Experimental Design ◽  
Objective Function ◽  
Response Surface ◽  
History Matching ◽  
Design Methodology ◽  
Production Data ◽  
Objective Functions ◽  
Flow Parameters ◽  
Production History

Summary History matching is an inverse problem in which an engineer calibrates key geological/fluid flow parameters by fitting a simulator's output to the real reservoir production history. It has no unique solution because of insufficient constraints. History-match solutions are obtained by searching for minima of an objective function below a preselected threshold value. Experimental design and response surface methodologies provide an efficient approach to build proxies of objective functions (OF) for history matching. The search for minima can then be easily performed on the proxies of OF as long as its accuracy is acceptable. In this paper, we first introduce a novel experimental design methodology for semi-automatically selecting the sampling points, which are used to improve the accuracy of constructed proxies of the nonlinear OF. This method is based on derivatives of constructed proxies. We propose an iterative procedure for history matching, applying this new design methodology. To obtain the global optima, the proxies of an objective function are initially constructed on the global parameter space. They are iteratively improved until adequate accuracy is achieved. We locate subspaces in the vicinity of the optima regions using a clustering technique to improve the accuracy of the reconstructed OF in these subspaces. We test this novel methodology and history-matching procedure with two waterflooded reservoir models. One model is the Imperial College fault model (Tavassoli et al. 2004). It contains a large bank of simulation runs. The other is a modified version of SPE9 (Killough 1995) benchmark problem. We demonstrate the efficiency of this newly developed history-matching technique. Introduction History matching (Eide et al. 1994; Landa and Güyagüler 2003) is an inverse problem in which an engineer calibrates key geological/fluid flow parameters of reservoirs by fitting a reservoir simulator's output to the real reservoir production history. It has no unique solution because of insufficient constraints. The traditional history matching is performed in a semi-empirical approach, which is based on the engineer's understanding of the field production behavior. Usually, the model parameters are adjusted using a one-factor-at-a-time approach. History matching can be very time consuming, because many simulation runs may be required for obtaining good fitting results. Attempts have been made to automate the history-matching process by using optimal control theory (Chen et al. 1974) and gradient techniques (Gomez et al. 2001). Also, design of experiment (DOE) and response surface methodologies (Eide et al. 1994; Box and Wilson 1987; Montgomery 2001; Box and Hunter 1957; Box and Wilson 1951; Damsleth et al. 1992; Egeland et al. 1992; Friedmann et al. 2003) (RSM) were introduced in the late 1990s to guide automatic history matching. The goal of these automatic methods is to achieve reasonably faster history-matching techniques than the traditional method. History matching is an optimization problem. The objective is to find the best of all possible sets of geological/fluid flow parameters to fit the production data of reservoirs. To assess the quality of the match, we define an OF (Atallah 1999). For history-matching problems, an objective function is usually defined as a distance (Landa and Güyagüler 2003) between a simulator's output and reservoir production data. History-matching solutions are obtained by searching for minima of the objective function. Experimental design and response surface methodologies provide an efficient approach to build up hypersurfaces (Kecman 2001) of objective functions (i.e., proxies of objective functions with a limited number of simulation runs for history matching). The search for minima can then be easily performed on these proxies as long as their accuracy is acceptable. The efficiency of this technique depends on constructing adequately accurate objective functions.

Probabilistic History Matching and Prediction of Production Performance by Waterflood: A Case Study of 70 Years Old Oil Field

2021 ◽  
Author(s):  
E. Noviyanto
Keyword(s):  
History Matching ◽  
Model Building ◽  
Dynamic Modelling ◽  
Oil Field ◽  
Production Performance ◽  
Recovery Factor ◽  
Real Field ◽  
Base Case ◽  
Work Activity ◽  
The Future

This paper presents the application of probabilistic history matching and prediction workflow in a real field case in Indonesia. The main objective of this novel approach is to capture the subsurface uncertainty for better reservoir understanding to be able to manage its risk and make a better decision for further field development. The field is very complex, with updated geological concept of multi-level reservoirs that has more than a hundred of wells and has been producing for 70 years. Existing multi-realization of static reservoir model was built to determine range of probabilistic cases of In-Place calculation as output. Variation of fluid contacts, lithology/facies distribution, porosity distribution and Net to Gross map are the main differences among these cases. Structural model and reservoir properties from three pre-defined cases were imported to the integrated software modelling tool, excluding water saturation model. The static-dynamic model building process were then recorded under common workflow for integration and automation of rebuilding variation model. For effective probabilistic model initialization,an automatic capillary pressure adjustment was chosen. Subsequently, experimental design and optimization were run to manage probabilistic history matching effectively. Parameter screening and ranking tool were also used to update uncertainty design for the next iteration. The number of history match variants were managed by applying acceptable match criteria and clusterization. Twenty equiprobable history matching variants were selected to be carried over to prediction phase and the three selected remaining oil saturation distribution maps were assessed for waterflood pattern design. Having reduced the uncertainty of parameters by history matching process, the prediction of base case and waterflood scenario were run for twenty unique variants. Incremental cumulative oil is in the range of 14.81 MMSTB to 16.96 MMSTB, equivalent to incremental recovery factor 5% to 5.4%. This range represents static and dynamic input parameter uncertainty that examined in this study. High side of recovery factor from waterflood scenario is 21.6% which indicates many remaining unswept oils. These results were used for work activity recommendation in the future to recover more hydrocarbon from the 70 years old oil field. This paper demonstrates the first application of probabilistic dynamic modelling in the company including a first-step endeavour to integrate static and dynamic variable uncertainty for this field. The workflow will be used as a guideline process for other field applications in the future.

Gravity Drainage System: Investigation and Field Development Using Geological Modelling and Reservoir Simulation

2021 ◽  
Author(s):  
Obinna Somadina Ezeaneche ◽  
Robinson Osita Madu ◽  
Ishioma Bridget Oshilike ◽  
Orrelo Jerry Athoja ◽  
Mike Obi Onyekonwu
Keyword(s):  
Reservoir Simulation ◽  
Structural Control ◽  
History Matching ◽  
Drainage System ◽  
Production Performance ◽  
Gravity Drainage ◽  
Base Case ◽  
Proper Understanding ◽  
Field Development ◽  
Reservoir Performance

Abstract Proper understanding of reservoir producing mechanism forms a backbone for optimal fluid recovery in any reservoir. Such an understanding is usually fostered by a detailed petrophysical evaluation, structural interpretation, geological description and modelling as well as production performance assessment prior to history matching and reservoir simulation. In this study, gravity drainage mechanism was identified as the primary force for production in reservoir X located in Niger Delta province and this required proper model calibration using variation of vertical anisotropic ratio based on identified facies as against a single value method which does not capture heterogeneity properly. Using structural maps generated from interpretation of seismic data, and other petrophysical parameters from available well logs and core data such as porosity, permeability and facies description based on environment of deposition, a geological model capturing the structural dips, facies distribution and well locations was built. Dynamic modeling was conducted on the base case model and also on the low and high case conceptual models to capture different structural dips of the reservoir. The result from history matching of the base case model reveals that variation of vertical anisotropic ratio (i.e. kv/kh) based on identified facies across the system is more effective in capturing heterogeneity than using a deterministic value that is more popular. In addition, gas segregated fastest in the high case model with the steepest dip compared to the base and low case models. An improved dynamic model saturation match was achieved in line with the geological description and the observed reservoir performance. Quick wins scenarios were identified and this led to an additional reserve yield of over 1MMSTB. Therefore, structural control, facies type, reservoir thickness and nature of oil volatility are key forces driving the gravity drainage mechanism.

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.

scholarly journals Skenario Pengembangan Untuk Meningkatkan Recovery Factor Pada Lapangan TR Lapisan X Dengan Menggunakan Simulasi Reservoir

PETRO ◽  
2018 ◽  
Vol 4 (4) ◽  
Author(s):  
Muhamad Taufan Azhari
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Control Volume ◽  
Base Case ◽  
Case Scenario ◽  
Injection Wells ◽  
Base Case Scenario ◽  
Development Scenarios ◽  
Production Wells ◽  
Grid Construction

<p>Reservoir simulation is an area of reservoir engineering in which computer models are used to predict the flow of fluids through porous media. Reservoir simulation process starts with several steps; data preparation, model and grid construction, initialization, history matching and prediction. Initialization process is done for matching OOIP or total initial hydrocarbon which fill reservoir with hydrocarbon control volume with volumetric method.</p><p>To aim the best encouraging optimum data, these development scenarios of TR Field Layer X will be predicted for 30 years (from 2014 until January 2044). Development scenarios in this study consist of 4 scenarios : Scenario 1 (Base Case), Scenario 2 (Base Case + Reopening non-active wells), Scenario 3 (scenario 2 + infill production wells), Scenario 4 (Scenario 2 + 5 spot pattern of infill injection wells).</p>

Influence of Additional Objective Functions on Uncertainty Reduction and History Matching

2018 ◽  
Author(s):  
Forlan La Rosa Almeida ◽  
Helena Nandi Formentin ◽  
Célio Maschio ◽  
Alessandra Davolio ◽  
Denis José Schiozer
Keyword(s):  
History Matching ◽  
Uncertainty Reduction ◽  
Objective Functions

scholarly journals Development and Validation of Stability-Indicating Method for Estimation of Chlorthalidone in Bulk and Tablets with the Use of Experimental Design in Forced Degradation Experiments

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Sandeep Sonawane ◽  
Sneha Jadhav ◽  
Priya Rahade ◽  
Santosh Chhajed ◽  
Sanjay Kshirsagar
Keyword(s):  
Experimental Design ◽  
Degradation Products ◽  
Forced Degradation ◽  
Variable Response ◽  
Response Factors ◽  
Pareto Chart ◽  
Stability Indicating Method ◽  
Accuracy And Precision ◽  
Full Factorial Experimental Design ◽  
Development And Validation

Chlorthalidone was subjected to various forced degradation conditions. Substantial degradation of chlorthalidone was obtained in acid, alkali, and oxidative conditions. Further full factorial experimental design was applied for acid and alkali forced degradation conditions, in which strength of acid/alkali, temperature, and time of heating were considered as independent variables (factors) and % degradation was considered as dependent variable (response). Factors responsible for acid and alkali degradation were statistically evaluated using Yates analysis and Pareto chart. Furthermore, using surface response curve, optimized 10% degradation was obtained. All chromatographic separation was carried out on Phenomenex HyperClone C 18 column (250 × 4.6 mm, 5 μ), using mobile phase comprising methanol : acetonitrile : phosphate buffer (20 mM) (pH 3.0 adjusted witho-phosphoric acid): 30 : 10 : 60% v/v. The flow rate was kept constant at 1 mL/min and eluent was detected at 241 nm. In calibration curve experiments, linearity was found to be in the range of 2–12 μg/mL. Validation experiments proved good accuracy and precision of the method. Also there was no interference of excipients and degradation products at the retention time of chlorthalidone, indicating specificity of the method.

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