Integrated Modelling and Performance Reviews Helps to Unlock New Opportunities in a 40-year-old Mature Field Under Waterflood

2021 ◽  
Author(s):  
Manish Kumar Choudhary ◽  
Gaurav Mahanti ◽  
Yogesh Rana ◽  
Sai Venkata Garimella ◽  
Arfan Ali ◽  
...  
Keyword(s):  
History Matching ◽  
Dynamic Modelling ◽  
Static Model ◽  
Surveillance Data ◽  
Oil Fields ◽  
Match Quality ◽  
History Match ◽  
Remaining Oil ◽  
Production History ◽  
Mature Field

Abstract Field X is one of largest oil fields in Brunei producing since 1970's. The field consists of a large faulted anticlinal structure of shallow marine Miocene sediments. The field has over 500 compartments and is produced under waterflood since 1980's through 400+ conduits over 50 platforms. A comprehensive review of water injection performance was attempted in 2019 to assess remaining oil and identify infill opportunities. Large uncertainties in reservoir properties, connectivity and fluid contacts required that data across multiple disciplines is integrated to identify new opportunities. It was recognized early on that integrated analysis of surveillance data and production history over 40 years will be critical for understanding field performance. Hence, reviews were first initiated using sand maps and analytical techniques. Tracer surveys, reservoir pressures, salinity measurements, Production Logging Tool (PLT) were all analyzed to understand waterflood progression and to define connectivity scenarios. A complete review of well logs, core data from over 30 wells and outcrop studies was carried out as part of modelling workflow. This understanding was used to construct a new facies-based static model. In parallel, key dynamic inputs like PVT analysis reports and special core analysis studies were analyzed to update dynamic modelling components. Prior to initiating the full field model history matching, a comprehensive impact analysis of the key dynamic uncertainties i.e., Production allocation, connectivity and varying aquifer strength etc. were conducted. An Assisted History Matching (AHM) workflow was attempted, which helped in identifying high impacting inputs which could be varied for history matching. Adjoint techniques were also used to identify other plausible geological scenarios. The integrated review helped in identifying over 50 new opportunities which potentially can increase recovery by over 10%. The new static model identified upsides in Stock Tank Oil Initially in Place (STOIIP) which if realized could further increase ultimate recoverable. The use of AHM assisted in reducing iterations and achieve multiple history matched models, which can be used to quantify forecast uncertainty. The new opportunities have helped to revitalize the mature field and has potential to almost increase the production by over 50%. A dedicated team is now maturing these opportunities. The robust methodology of integrating surveillance data with simulation modelling as described in this paper is generic and could be useful in current day brown field development practices to serve as an effective and economic manner for sustaining oil production and maximizing ultimate recovery. It is essential that all surveillance and production history data are well analyzed together prior to attempting any detailed modelling exercise. New models should then be constructed which confirm to the surveillance information and capture reservoir uncertainties. In large oil fields with long production history with allocation uncertainties, it is always a challenge for a quantitative assessment of History match quality and infill well Ultimate Recovery (UR) estimations. Hence a composite History Match Quality Indicator (HMQI) was designed with an appropriate weightage of rate, cumulative & reservoir pressure mismatch, water breakthrough timing delays. Then HMQI parameter spatial variation maps were made for different zones over the entire field for understanding and appropriately discounting each infill well oil recovery. Also, it is critical that facies variation is properly captured in models to better understand waterfront movements and locate remaining oil. Dynamic modelling of mature field with long production history can be quite challenging on its own and it is imperative that new numerical techniques are used to increase efficiency.

Compressed Dimension of Reservoir Models Uncertainty Parameters for Optimized Model Calibration and History Matching Process

2021 ◽  
Author(s):  
Ali Al-Turki ◽  
Obai Alnajjar ◽  
Majdi Baddourah ◽  
Babatunde Moriwawon
Keyword(s):  
Model Calibration ◽  
History Matching ◽  
Fine Tuning ◽  
Global History ◽  
Match Quality ◽  
History Match ◽  
Matching Process ◽  
Reservoir Models ◽  
Field Level ◽  
Well Level

Abstract The algorithms and workflows have been developed to couple efficient model parameterization with stochastic, global optimization using a Multi-Objective Genetic Algorithm (MOGA) for global history matching, and coupled with an advanced workflow for streamline sensitivity-based inversion for fine-tuning. During parameterization the low-rank subsets of most influencing reservoir parameters are identified and propagated to MOGA to perform the field-level history match. Data misfits between the field historical data and simulation data are calculated with multiple realizations of reservoir models that quantify and capture reservoir uncertainty. Each generation of the optimization algorithms reduces the data misfit relative to the previous iteration. This iterative process continues until a satisfactory field-level history match is reached or there are no further improvements. The fine-tuning process of well-connectivity calibration is then performed with a streamlined sensitivity-based inversion algorithm to locally update the model to reduce well-level mismatch. In this study, an application of the proposed algorithms and workflow is demonstrated for model calibration and history matching. The synthetic reservoir model used in this study is discretized into millions of grid cells with hundreds of producer and injector wells. It is designed to generate several decades of production and injection history to evaluate and demonstrate the workflow. In field-level history matching, reservoir rock properties (e.g., permeability, fault transmissibility, etc.) are parameterized to conduct the global match of pressure and production rates. Grid Connectivity Transform (GCT) was used and assessed to parameterize the reservoir properties. In addition, the convergence rate and history match quality of MOGA was assessed during the field (global) history matching. Also, the effectiveness of the streamline-based inversion was evaluated by quantifying the additional improvement in history matching quality per well. The developed parametrization and optimization algorithms and workflows revealed the unique features of each of the algorithms for model calibration and history matching. This integrated workflow has successfully defined and carried uncertainty throughout the history matching process. Following the successful field-level history match, the well-level history matching was conducted using streamline sensitivity-based inversion, which further improved the history match quality and conditioned the model to historical production and injection data. In general, the workflow results in enhanced history match quality in a shorter turnaround time. The geological realism of the model is retained for robust prediction and development planning.

Local Update of Object-Based Geomodels

2009 ◽  
Vol 12 (03) ◽  
pp. 446-454 ◽  
Author(s):  
Frode Georgsen ◽  
Anne R. Syversveen ◽  
Ragnar Hauge ◽  
Jan I. Tollefsrud ◽  
Morten Fismen
Keyword(s):  
History Matching ◽  
Reservoir Management ◽  
Static Model ◽  
History Match ◽  
New Model ◽  
Reservoir Models ◽  
New Information ◽  
Object Based ◽  
Object Models ◽  
New Algorithms

Summary The possibility of updating reservoir models with new well information is important for good reservoir management. The process of drilling a new well through to update of the static model and to history match the new model is often a time-consuming process. This paper presents new algorithms that allow the rapid updating of object-based facies models by further development of already existing models. An existing facies realization is adjusted to match new well observations by changing objects locally or adding/removing objects if required. Parts of the realization that are not influenced by the new wells are not changed. A local update of a specified region of the reservoir can be performed, leaving the rest of the reservoir unchanged or with minimum change because of new wells. In this method, the main focus is the algorithm implemented to fulfill well conditioning. The effect of this algorithm on different object models is presented through several case studies. These studies show how the local update consistently includes new information while leaving the rest of the realization unperturbed, thereby preserving the good history match. Introduction Rapid updating of static and dynamic reservoir models is important for reservoir management. Continual maintenance of history-matched models allows for right-time decisions to optimize the reservoir performance. The process of drilling a new well through to updating of the static model and history matching of the new model is often a time-consuming process. Static reservoir models and history matches are updated only intermittently, and there is typically a 1- to 2-year delay between the drilling of a new well and the generation of a reliable history-matched model that incorporates the new information. This paper presents new algorithms that allow rapid updating of static reservoir models when new wells are drilled. The static-model update is designed to keep as much of the existing history match as possible by locally adjusting the existing static model to the new well data. As the name implies, object models use a set of facies objects to generate a facies realization. Stochastic object-modeling algorithms have been developed to improve the representation of facies architectures in complex heterogeneous reservoirs and, thereby, to obtain more-realistic dynamic behavior of the reservoir models. We consider the main advantages of object models to be the ability to create geologically realistic facies elements (objects) and control the interaction between them, to correlate observations between wells (connectivity) explicitly, and the possibility of applying intraobject petrophysical trends.

Unlocking Ensemble History Matching Potential with Parallelism and Careful Data Management

2021 ◽  
Author(s):  
Giorgio Fighera ◽  
Ernesto Della Rossa ◽  
Patrizia Anastasi ◽  
Mohammed Amr Aly ◽  
Tiziano Diamanti
Keyword(s):  
History Matching ◽  
Extreme Values ◽  
Computation Time ◽  
Third Party ◽  
Computational Time ◽  
Match Quality ◽  
History Match ◽  
Initial Ensemble ◽  
Computational Resources

Abstract Improvements in reservoir simulation computational time thanks to GPU-based simulators and the increasing computational power of modern HPC systems, are paving the way for a massive employment of Ensemble History Matching (EHM) techniques which are intrinsically parallel. Here we present the results of a comparative study between a newly developed EHM tool that aims at leveraging the GPU parallelism, and a commercial third-party EHM software as a benchmark. Both are tested on a real case. The reservoir chosen for the comparison has a production history of 3 years with 15 wells between oil producers, and water and gas injectors. The EHM algorithm used is the Ensemble Smoother with Multiple Data Assimilations (ESMDA) and both tools have access to the same computational resources. The EHM problem was stated in the same way for both tools. The objective function considers well oil productions, water cuts, bottom-hole pressures, and gas-oil-ratios. Porosity and horizontal permeability are used as 3D grid parameters in the update algorithm, along with nine scalar parameters for anisotropy ratios, Corey exponents, and fault transmissibility multipliers. Both the presented tool and the benchmark obtained a satisfactory history match quality. The benchmark tool took around 11.2 hours to complete, while the proposed tool took only 1.5 hours. The two tools performed similar updates on the scalar parameters with only minor discrepancies. Updates on the 3D grid properties instead show significant local differences. The updated ensemble for the benchmark reached extreme values for porosity and permeability which are also distributed in a heterogeneous way. These distributions are quite unlikely in some model regions given the initial geological characterization of the reservoir. The updated ensemble for the presented tool did not reach extreme values in neither porosity nor permeability. The resulting property distributions are not so far off from the ones of the initial ensemble, therefore we can conclude that we were able to successfully update the ensemble while persevering the geological characterization of the reservoir. Analysis suggests that this discrepancy is due to the different way by which our EHM code consider inactive cells in the grid update calculations compared to the benchmark highlighting the fact that statistics including inactive cells should be carefully managed to correctly preserve the geological distribution represented in the initial ensemble. The presented EHM tool was developed from scratch to be fully parallel and to leverage on the abundantly available computational resources. Moreover, the ESMDA implementation was tweaked to improve the reservoir update by carefully managing inactive cells. A comparison against a benchmark showed that the proposed EHM tool achieved similar history match quality while improving the computation time and the geological realism of the updated ensemble.

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.

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