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.

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.

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.

Seismic petrophysics focused case study for AVA modelling and pre-stack seismic inversion

2016 ◽  
Vol 56 (1) ◽  
pp. 341
Author(s):  
Jahan Zeb ◽  
Sanjeev Rajput ◽  
Jimmy Ting
Keyword(s):  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Oil Field ◽  
Well Logs ◽  
Real Field ◽  
Seismic Responses ◽  
Reservoir Properties ◽  
Rock Types ◽  
Amplitude Versus

Hydrocarbon reservoirs are characterised by integrating seismic, well-log and petrophysical information, which are dissimilar in spatial distribution, scale and relationship to reservoir properties. Well logs are essential for amplitude versus offset (AVO) modelling and seismic inversion. The usability of well logs can be determined during wavelet estimation, seismic-to-well ties, background model building, property distribution for inversion, deriving probability density functions and variograms, offset-to-angle conversion of seismic data, and many other processes. For the implementation of seismic inversion workflows, accurate and geologically corrected compressional-sonic, shear-sonic and density logs are necessary. Preparing the logs for quantitative interpretation becomes challenging in a real-field environment because of bad borehole conditions including washouts, uncalibrated and variability of logging tools, invasion effects, missing shear logs and change of borehole size. Conventional petrophysical analysis is usually restricted to the reservoir interval, the calculation of reservoir versus non-reservoir (including sands or shales), and log corrections for smaller intervals; in contrast, seismic petrophysics encompasses the entire geological interval, calculates the volume of multi-minerals, incorporates boundaries between non-reservoir and reservoir, and often includes the prediction of missing compressional and shear-sonic for AVO analysis. A detailed seismic petrophysics analysis was performed for amplitude versus angle (AVA) modelling and attributes analysis. To perform the AVA modelling, a series of forward models in association with rock physics modelled fluid-substituted logs were developed, and associated seismic responses for various pore fluids and rock types studied. The results reveal that synthetic seismic responses together with the AVA analysis show changes for various lithologies. AVA attributes analysis show trends in generated synthetic seismic responses for various fluid-substituted and porosity logs. Reservoir modelling and fluid substitution increases understanding of the observed seismic response. This paper describes detailed data analysis using various techniques to confirm the rock model for petrophysical evaluation, rock physics modelling, AVA analysis, pre-stack seismic inversion, and the scenario modelling applied to the study of an oil field in Australia.

Gas Lift Optimization of an Oil Field in Malaysia

2014 ◽  
Vol 974 ◽  
pp. 367-372
Author(s):  
Nurul Aimi Ghazali ◽  
T.A.T. Mohd ◽  
N. Alias ◽  
E. Yahya ◽  
M.Z. Shahruddin ◽  
...  
Keyword(s):  
Hydrostatic Pressure ◽  
Gas Injection ◽  
Oil Field ◽  
Production Performance ◽  
Base Case ◽  
External Energy ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Gas Lift

Gas lift is an artificial lift method which is commonly used in offshore operation with sufficient gas sources as it consumes minimum space on the platform. Gas lift operates by injecting a high pressure gas down through the tubing casing annulus of a well and the injected gas enters the tubing through a gas lift valve installed on the tubing. Gas lift increases production by two means, density reduction of oil column inside the tubing so that the flowing bottom-hole pressure which is affected by the hydrostatic pressure of the fluid column is reduced and by providing external energy to the oil as the gas expends.Reducing the bottom-hole pressure will improve the drawdown of the well. A production well is modelled by using a production modelling program, Integrated Production Modeling (IPM) Prosper to analyze the production performance at various conditions. A base case model is developed from the production data of an actual oil field to simulate the performance of the actual well without gas lift system. Later, the gas lift is added to the model and the performance was compared with the base case model. The gas parameter was also studied to determine the optimum injection gas condition for maximum oil production. The gas injected at 1490m can be achieved by injecting the gas with 1200 psi, l300 psi or 1400 psi. However, the optimum gas injection pressure was determined to be at 1400 psi as the design shows that the required unloading stage is the least. The optimum gas injection rate was determined at 5 MMscf/d with the estimated net revenue is the highest. For injection gas gravity, the lighter gas was determined to be the optimum selection since it gives significant reduction of FBHP (Flowing Bottom Hole Pressure) with less hydrostatic pressure inside the tubing column.

AN ANALYSIS OF THE PRODUCTION PERFORMANCE OF THE WINDALIA SAND RESERVOIR OF THE BARROW ISLAND OIL FIELD

1977 ◽  
Vol 17 (1) ◽  
pp. 105 ◽  
Author(s):  
C. T. Williams
Keyword(s):  
Oil Recovery ◽  
History Matching ◽  
Water Injection ◽  
Feasibility Studies ◽  
Oil Field ◽  
Production Performance ◽  
High Porosity ◽  
Injection Wells ◽  
Well Completion ◽  
East West

The Windalia Sand is a high porosity, low permeability oil reservoir. Currently 454 wells penetrate the unit for production or water injection operations, and are drilled on a north-south, east-west 16 ha (40 ac.) spacing. Early production performance data indicated a trend of water break-through into wells located east and west of water injection wells in an inverted nine-spot pattern. This early trend has continued and the east- west break-through has become more widespread with time. It was recognised that it could be possible to improve the performance of the waterflood if the factors causing the phenomenon were able to be identified. A detailed geological review of well data was initiated to investigate causes and possible controls of the phenomenon and to determine if oil recovery could be improved. This work was augmented by an engineering study of production data. Subsequently, a computer model was developed to investigate the simulated effects of changes to well patterns on the field's production performance.The geological review determined that the reservoir contains significant local and transitional irregularities (or inhomogeneities). The mapping of a number of reservoir parameters has shown there are genetic patterns or trends and these are postulated as being at least partial controls of preferential direction of fluid movement.Previously the reservoir had been regarded as being a more uniform "layer-cake" sand. Well completion practices and timing together with production and injection methods are thought to have accentuated the latent genetic controls. Imposed pressure parting has been postulated, on engineering premises, as a control of fluid movement. The modelling study used the notion of anisotropic permeability in attempting to history-match production performances.Because of the reservoir size and anisotropy it was impractical to model the entire field. Selected type areas within the reservoir were studied. Good history-matching of various well types (based on location within a pattern) was possible. Predictions of production performance can be made for various simulated pattern changes allowing feasibility studies to be made of possible conversion programs.East-west producing wells are being converted to injectors as they water out. This program has converted part of the reservoir to a line-drive injection configuration and improved performance in these areas is evident.

scholarly journals The effect of gravity drainage mechanism on oil recovery by reservoir simulation; a case study in an Iranian highly fractured reservoir

2021 ◽  
Author(s):  
K. Zobeidi ◽  
M. Mohammad-Shafie ◽  
M. Ganjeh-Ghazvini
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
Fractured Reservoirs ◽  
Saturation Pressure ◽  
Oil Field ◽  
Recovery Factor ◽  
Real Field ◽  
Gravity Drainage ◽  
Special Importance ◽  
Fractured Reservoir

AbstractA comprehensive reservoir simulation study was performed on an oil field that had a wide fracture network and could be considered a typical example of highly fractured reservoirs in Iran. This field is located in southwest of Iran in Zagros sedimentary basin among several neighborhood fields with relatively considerable fractured networks. In this reservoir, the pressure drops below the saturation pressure and causes the formation of a secondary gas cap. This can help to better assess the gravity drainage phenomenon. We decided to investigate and track the effect of gravity drainage mechanism on the recovery factor of oil production in this field. In this study, after/before the implementation of gas injection scenarios with different discharges, the contribution of gravity drainage mechanism to the recovery factor was found more than 50%. Considering that a relatively large number of studies have been conducted on this field simultaneously with the growth of information from different aspects and this study is the last and most comprehensive study and also the results are extracted from real field data using existing reservoir simulators, it is of special importance and can be used by researchers.

scholarly journals Pengembangan Lapangan “Y” Menggunakan Simulasi Reservoir

2017 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Lia Yunita
Keyword(s):  
Data Collection ◽  
History Matching ◽  
Computer Modelling ◽  
Data Entry ◽  
Recovery Factor ◽  
Production Data ◽  
Base Case ◽  
Case Scenario ◽  
Base Case Scenario ◽  
Cumulative Production

<p>Lapangan “Y” ditemukan melalui sumur pengeboran eksplorasi PMS 01 yang dibor pada 18 April 1980 dan diselesaikan pada 31 Juli 1980.Hal ini menyebabkan timbulnya pemikiran bagaimana strategi untuk mengembangkan lapangan guna meningkatkan <em>recovery factor.</em>Dalam menyelesaikan permasalahan ini dilakukan simulasi reservoir. Simulator yang digunakan adalah <em>CMG-GEM </em>yang dibuat oleh <em>Computer Modelling Group Ltd., Calgary, Canada</em>. Simulator tersebut adalah simulator jenis komposisional.Langkah awal dalam tahap simulasi adalah pengumpulan, persiapan, dan pengolahan data. Pengumpulan data meliputi data geologi, batuan, fluida, ekuilibrium  dan data produksi. Proses inisialisasi merupakan tahapan setelah pemasukkan data yaitu proses pengkondisian model supaya selaras dengan kondisi awal reservoir yaitu dengan menyelaraskan OGIP hasil perhitungan simulator dengan perhitungan volumetrik. Proses inisialisasi menghasilkan harga OGIP simulasi sebesar 23.03 Bscf dan untuk perhitungan volumetrik adalah 23.07 Bcsf, hal ini menunjukan perbedaan kurang dari 1 %. Perbedaan yang sangat kecil tersebut memperlihatkan bahwa hasil simulasi sudah sangat memadai. Validasi data juga dilakukan dengan proses <em>history matching</em> (penyelarasan). Proses penyelarasan data produksi (laju produksi terhadap waktu dan kumulatif produksi terhadap waktu) dan tekanan menghasilkan kurva yang selaras.Peramalan perilaku produksi reservoir dilakukan dengan membuat beberapa skenario produksi. Ada usulan tiga skenario, yaitu Skenario A, reservoir diproduksikan oleh satu sumur PMS 01 dengan membuka perforasi pada zona 12 dan zona 15 (<em>base case</em>), Skenario B, reservoir diproduksikan oleh PMS 01 dengan membuka perforasi pada zona 12, zona 15 dan zona 16. Skenario C, reservoir diproduksikan oleh dua sumur yaitu sumur PMS 01 (zona 12, zona 15 dan zona16) dan sumur PMS 03 (zona 12, zona 15 dan zona 16). Berdasarkan skenario yang dilakukan diperoleh kumulatif produksi terbesar pada skenario C sebesar 16.2 Bscf atau dengan <em>recovery factor</em> sebesar 70.22 %.</p><p><em>The "Y" field was discovered through an exploration drilling well PMS 01 which was drilled on April 18, 1980 and completed on July 31, 1980. This led to the emergence of ideas on how to develop a field to improve recovery factors. In solving this problem reservoir simulations were carried out. The simulator used is the CMG-GEM made by Computer Modeling Group Ltd., Calgary, Canada. The simulator is a compositional type simulator. The first step in the simulation stage is data collection, preparation, and processing. Data collection includes geological, rock, fluid, equilibrium and production data. The initialization process is the stage after data entry, namely the model conditioning process so that it is aligned with the initial reservoir conditions by aligning the OGIP results of the simulator calculation with the volumetric calculation. The initialization process produces a simulation OGIP price of 23.03 Bscf and for volumetric calculations is 23.07 Bcsf, this shows a difference of less than 1%. The small difference shows that the simulation results are very adequate. Data validation is also carried out with the history matching process. The process of aligning production data (production rate with respect to time and cumulative production with respect to time) and pressure produces a harmonious curve. Forecasting of reservoir production behavior is carried out by creating several production scenarios. There are three proposed scenarios, namely Scenario A, the reservoir is produced by one well PMS 01 by opening perforation in zone 12 and zone 15 (base case), Scenario B, the reservoir is produced by PMS 01 by opening the perforation in zone 12, zone 15 and zone 16 Scenario C, the reservoir is produced by two wells namely PMS 01 wells (zone 12, zone 15 and zone16) and PMS 03 wells (zone 12, zone 15 and zone 16). Based on the scenario, the largest cumulative production obtained in scenario C is 16.2 Bscf or with a recovery factor of 70.22%.</em></p>

scholarly journals Efficient Dimensionality Reduction Methods in Reservoir History Matching

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3137
Author(s):  
Amine Tadjer ◽  
Reider B. Bratvold ◽  
Remus G. Hanea
Keyword(s):  
Data Assimilation ◽  
Dimensionality Reduction ◽  
Gaussian Process ◽  
Latent Variable ◽  
History Matching ◽  
Production Performance ◽  
Latent Variable Model ◽  
Variable Model ◽  
Multiple Data ◽  
Ensemble Smoother

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

scholarly journals Injection-production optimization of carbonate reservoir based on an inter-well connectivity model

2021 ◽  
pp. 014459872199465
Author(s):  
Yuhui Zhou ◽  
Sheng Lei ◽  
Xuebiao Du ◽  
Shichang Ju ◽  
Wei Li
Keyword(s):  
History Matching ◽  
Water Injection ◽  
Field Application ◽  
Carbonate Reservoir ◽  
Production Optimization ◽  
Oil Field ◽  
Carbonate Reservoirs ◽  
Utilization Rate ◽  
Production Volume ◽  
Connectivity Model

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.

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