Full-Field History-Matching of Commingling Stacked Reservoirs: A Case Study of an Oman Southern Asset

2021 ◽  
Author(s):  
Samuel Aderemi ◽  
Husain Ali Al Lawati ◽  
Mansura Khalfan Al Rawahy ◽  
Hassan Kolivand ◽  
Manish Kumar Singh ◽  
...  
Keyword(s):  
History Matching ◽  
Water Saturation ◽  
Uncertain Parameters ◽  
Water Production ◽  
Full Field ◽  
Matching Process ◽  
Model Response ◽  
Fluid Properties ◽  
Acceptable Model ◽  
Input Uncertainties

Abstract This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties. It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics. The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.

History Matching and Production Forecast With Logs as Effective Completion and Reservoir-Managing Tools in Horizontal and Vertical Wells

2012 ◽  
Vol 15 (05) ◽  
pp. 596-608
Author(s):  
Carlos F. Haro
Keyword(s):  
History Matching ◽  
Water Saturation ◽  
Material Balance ◽  
Reservoir Management ◽  
Production Forecast ◽  
Arrival Times ◽  
Irreducible Water Saturation ◽  
Fluid Properties ◽  
Horizontal And Vertical Wells ◽  
The Right

Summary Simulation history matching is a daunting, time-consuming task with numerous unknowns and several plausible answers. Scale differences in the data frequently obscure results, limiting its application in completion strategies. Good history matching does not guarantee accurate production forecasts, however. Reliable predictions, required for well planning, depend on the ability of the user to reduce the uncertainties to find consistent and timely solutions. Logs can provide appropriate conditioning data for history matching to enable its use for reservoir management. Electrofacies, capillary pressure, and absolute and relative permeability, imprinted on logs, can be mathematically linked with irreducible water saturation (Swi). Unlike reservoir simulators, well logs are at the right scale for completion designs. Logs facilitate upscaling, honoring rock and fluid properties and the physics of flow (Haro 2006). Logs are snapshot measurements that are amenable for conversion into dynamic forecasting tools by use of flow and pressure equations. This concept permits creation of synthetic production logs (SPLTs) over time, from which production decline can be calculated. This method consists of integrating material balance, flow/ pressure algorithms, fluid data, cores, and log data. Thus, the corresponding analytical expressions are required. In this approach, every well represents a finite, gridded tank, capable of producing a measurable volume of fluids, limited by its petrophysical constraints. Superposition, in terms of pressure and flow, combines the various components within and among wells. The quality of the results is ensured because material balance must be honored at every depth at all times under different production scenarios and the prevailing drive mechanism. This log-handling technique helps when making strategic economic decisions to maximize reserves and optimize the reservoir-development plan. This strategy is used to obtain oil in place (OIP), drainage radii, lateral connectivity, fluid-bank arrival times, productivity indices (PIs), inflow performance relationship (IPR), production allocation, and recovery per zone per well. Current log analyses or simulators generally do not provide these parameters at this detail. This refined use of logs streamlines completion designs and improves conformance, enabling us to comply with an important part of daily reservoir management.

Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

2011 ◽  
Vol 14 (05) ◽  
pp. 621-633 ◽  
Author(s):  
Alireza Kazemi ◽  
Karl D. Stephen ◽  
Asghar Shams
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Production Data ◽  
Similar Degree ◽  
Full Field ◽  
Matching Process ◽  
Production History ◽  
Induced Changes ◽  
4D Seismic

Summary History matching of a reservoir model is always a difficult task. In some fields, we can use time-lapse (4D) seismic data to detect production-induced changes as a complement to more conventional production data. In seismic history matching, we predict these data and compare to observations. Observed time-lapse data often consist of relative measures of change, which require normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching. We apply the approach to the Nelson field in which four surveys are available over 9 years of production. We normalize the 4D signature in a number of ways. First, we use predictions of 4D signature from vertical wells that match production, and we derive a normalization function. As an alternative, we use crossplots of the full-field prediction against observation. Normalized observations are used in an automatic-history-matching process, in which the model is updated. We analyze the results of the two normalization approaches and compare against the case of just using production data. The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8 and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods, respectively. When we use only production data, the production history match is improved to a similar degree (45%), but in predictions, the improvement is only 25% and the 4D seismic misfit is 10% worse. Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching, we can better predict reservoir behavior and identify regions of remaining oil.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

scholarly journals Wettability Effects on Oil-Recovery Mechanisms in Fractured Reservoirs

2001 ◽  
Vol 4 (06) ◽  
pp. 455-466 ◽  
Author(s):  
A. Graue ◽  
T. Bognø ◽  
B.A. Baldwin ◽  
E.A. Spinler
Keyword(s):  
Crude Oil ◽  
Numerical Simulations ◽  
Capillary Pressure ◽  
Oil Recovery ◽  
History Matching ◽  
Water Saturation ◽  
Fracture Network ◽  
Stock Tank ◽  
Recovery Mechanisms

Summary Iterative comparison between experimental work and numerical simulations has been used to predict oil-recovery mechanisms in fractured chalk as a function of wettability. Selective and reproducible alteration of wettability by aging in crude oil at an elevated temperature produced chalk blocks that were strongly water-wet and moderately water-wet, but with identical mineralogy and pore geometry. Large scale, nuclear-tracer, 2D-imaging experiments monitored the waterflooding of these blocks of chalk, first whole, then fractured. This data provided in-situ fluid saturations for validating numerical simulations and evaluating capillary pressure- and relative permeability-input data used in the simulations. Capillary pressure and relative permeabilities at each wettability condition were measured experimentally and used as input for the simulations. Optimization of either Pc-data or kr-curves gave indications of the validity of these input data. History matching both the production profile and the in-situ saturation distribution development gave higher confidence in the simulations than matching production profiles only. Introduction Laboratory waterflood experiments, with larger blocks of fractured chalk where the advancing waterfront has been imaged by a nuclear tracer technique, showed that changing the wettability conditions from strongly water-wet to moderately water-wet had minor impact on the the oil-production profiles.1–3 The in-situ saturation development, however, was significantly different, indicating differences in oil-recovery mechanisms.4 The main objective for the current experiments was to determine the oil-recovery mechanisms at different wettability conditions. We have reported earlier on a technique that reproducibly alters wettability in outcrop chalk by aging the rock material in stock-tank crude oil at an elevated temperature for a selected period of time.5 After applying this aging technique to several blocks of chalk, we imaged waterfloods on blocks of outcrop chalk at different wettability conditions, first as a whole block, then when the blocks were fractured and reassembled. Earlier work reported experiments using an embedded fracture network,4,6,7 while this work also studied an interconnected fracture network. A secondary objective of these experiments was to validate a full-field numerical simulator for prediction of the oil production and the in-situ saturation dynamics for the waterfloods. In this process, the validity of the experimentally measured capillary pressure and relative permeability data, used as input for the simulator, has been tested at strongly water-wet and moderately water-wet conditions. Optimization of either Pc data or kr curves for the chalk matrix in the numerical simulations of the whole blocks at different wettabilities gave indications of the data's validity. History matching both the production profile and the in-situ saturation distribution development gave higher confidence in the simulations of the fractured blocks, in which only the fracture representation was a variable. Experimental Rock Material and Preparation. Two chalk blocks, CHP8 and CHP9, approximately 20×12×5 cm thick, were obtained from large pieces of Rørdal outcrop chalk from the Portland quarry near Ålborg, Denmark. The blocks were cut to size with a band saw and used without cleaning. Local air permeability was measured at each intersection of a 1×1-cm grid on both sides of the blocks with a minipermeameter. The measurements indicated homogeneous blocks on a centimeter scale. This chalk material had never been contacted by oil and was strongly water-wet. The blocks were dried in a 90°C oven for 3 days. End pieces were mounted on each block, and the whole assembly was epoxy coated. Each end piece contained three fittings so that entering and exiting fluids were evenly distributed with respect to height. The blocks were vacuum evacuated and saturated with brine containing 5 wt% NaCl+3.8 wt% CaCl2. Fluid data are found in Table 1. Porosity was determined from weight measurements, and the permeability was measured across the epoxy-coated blocks, at 2×10–3 µm2 and 4×10–3 µm2, for CHP8 and CHP9, respectively (see block data in Table 2). Immobile water saturations of 27 to 35% pore volume (PV) were established for both blocks by oilflooding. To obtain uniform initial water saturation, Swi, oil was injected alternately at both ends. Oilfloods of the epoxy-coated block, CHP8, were carried out with stock-tank crude oil in a heated pressure vessel at 90°C with a maximum differential pressure of 135 kPa/cm. CHP9 was oilflooded with decane at room temperature. Wettability Alteration. Selective and reproducible alteration of wettability, by aging in crude oil at elevated temperatures, produced a moderately water-wet chalk block, CHP8, with similar mineralogy and pore geometry to the untreated strongly water-wet chalk block CHP9. Block CHP8 was aged in crude oil at 90°C for 83 days at an immobile water saturation of 28% PV. A North Sea crude oil, filtered at 90°C through a chalk core, was used to oilflood the block and to determine the aging process. Two twin samples drilled from the same chunk of chalk as the cut block were treated similar to the block. An Amott-Harvey test was performed on these samples to indicate the wettability conditions after aging.8 After the waterfloods were terminated, four core plugs were drilled out of each block, and wettability measurements were conducted with the Amott-Harvey test. Because of possible wax problems with the North Sea crude oil used for aging, decane was used as the oil phase during the waterfloods, which were performed at room temperature. After the aging was completed for CHP8, the crude oil was flushed out with decahydronaphthalene (decalin), which again was flushed out with n-decane, all at 90°C. Decalin was used as a buffer between the decane and the crude oil to avoid asphalthene precipitation, which may occur when decane contacts the crude oil.

A Robust Methodology To Simulate Water-Alternating-Gas Experiments at Different Scenarios Under Near-Miscible Conditions

SPE Journal ◽  
2017 ◽  
Vol 22 (05) ◽  
pp. 1506-1518 ◽  
Author(s):  
Pedram Mahzari ◽  
Mehran Sohrabi
Keyword(s):  
Porous Media ◽  
History Matching ◽  
Flow In Porous Media ◽  
Phase Flow ◽  
Two Phase ◽  
Matching Process ◽  
Water Alternating Gas ◽  
Three Phase ◽  
Three Phase Flow ◽  
Cycle Dependent

Summary Three-phase flow in porous media during water-alternating-gas (WAG) injections and the associated cycle-dependent hysteresis have been subject of studies experimentally and theoretically. In spite of attempts to develop models and simulation methods for WAG injections and three-phase flow, current lack of a solid approach to handle hysteresis effects in simulating WAG-injection scenarios has resulted in misinterpretations of simulation outcomes in laboratory and field scales. In this work, by use of our improved methodology, the first cycle of the WAG experiments (first waterflood and the subsequent gasflood) was history matched to estimate the two-phase krs (oil/water and gas/oil). For subsequent cycles, pertinent parameters of the WAG hysteresis model are included in the automatic-history-matching process to reproduce all WAG cycles together. The results indicate that history matching the whole WAG experiment would lead to a significantly improved simulation outcome, which highlights the importance of two elements in evaluating WAG experiments: inclusion of the full WAG experiments in history matching and use of a more-representative set of two-phase krs, which was originated from our new methodology to estimate two-phase krs from the first cycle of a WAG experiment. Because WAG-related parameters should be able to model any three-phase flow irrespective of WAG scenarios, in another exercise, the tuned parameters obtained from a WAG experiment (starting with water) were used in a similar coreflood test (WAG starting with gas) to assess predictive capability for simulating three-phase flow in porous media. After identifying shortcomings of existing models, an improved methodology was used to history match multiple coreflood experiments simultaneously to estimate parameters that can reasonably capture processes taking place in WAG at different scenarios—that is, starting with water or gas. The comprehensive simulation study performed here would shed some light on a consolidated methodology to estimate saturation functions that can simulate WAG injections at different scenarios.

scholarly journals Interpretation of Gas/Water Relative Permeability of Coal Using the Hybrid Bayesian-Assisted History Matching: New Insights

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 626
Author(s):  
Jiyuan Zhang ◽  
Bin Zhang ◽  
Shiqian Xu ◽  
Qihong Feng ◽  
Xianmin Zhang ◽  
...  
Keyword(s):  
Relative Permeability ◽  
Coalbed Methane ◽  
History Matching ◽  
Water Saturation ◽  
Unsteady State ◽  
Core Flooding ◽  
Rock Interaction ◽  
Interaction Mechanisms ◽  
True Value ◽  
Assisted History Matching

The relative permeability of coal to gas and water exerts a profound influence on fluid transport in coal seams in both primary and enhanced coalbed methane (ECBM) recovery processes where multiphase flow occurs. Unsteady-state core-flooding tests interpreted by the Johnson–Bossler–Naumann (JBN) method are commonly used to obtain the relative permeability of coal. However, the JBN method fails to capture multiple gas–water–coal interaction mechanisms, which inevitably results in inaccurate estimations of relative permeability. This paper proposes an improved assisted history matching framework using the Bayesian adaptive direct search (BADS) algorithm to interpret the relative permeability of coal from unsteady-state flooding test data. The validation results show that the BADS algorithm is significantly faster than previous algorithms in terms of convergence speed. The proposed method can accurately reproduce the true relative permeability curves without a presumption of the endpoint saturations given a small end-effect number of <0.56. As a comparison, the routine JBN method produces abnormal interpretation results (with the estimated connate water saturation ≈33% higher than and the endpoint water/gas relative permeability only ≈0.02 of the true value) under comparable conditions. The proposed framework is a promising computationally effective alternative to the JBN method to accurately derive relative permeability relations for gas–water–coal systems with multiple fluid–rock interaction mechanisms.

Work Flow History Matches Numerical Simulation Models of Fractured Shale Wells

2021 ◽  
Vol 73 (04) ◽  
pp. 60-61
Author(s):  
Chris Carpenter
Keyword(s):  
Numerical Simulation ◽  
Fracture Toughness ◽  
Hydraulic Fracturing ◽  
History Matching ◽  
Simulation Models ◽  
Work Flow ◽  
Fracture Length ◽  
Matching Process ◽  
Rate Transient Analysis ◽  
Reservoir Simulator

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199149, “Rate-Transient-Analysis-Assisted History Matching With a Combined Hydraulic Fracturing and Reservoir Simulator,” by Garrett Fowler, SPE, and Mark McClure, SPE, ResFrac, and Jeff Allen, Recoil Resources, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, originally scheduled to be held in Bogota, Colombia, 17–19 March. The paper has not been peer reviewed. This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term “motifs” that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. Introduction This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199149, “Rate-Transient-Analysis-Assisted History Matching With a Combined Hydraulic Fracturing and Reservoir Simulator,” by Garrett Fowler, SPE, and Mark McClure, SPE, ResFrac, and Jeff Allen, Recoil Resources, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, originally scheduled to be held in Bogota, Colombia, 17-19 March. The paper has not been peer reviewed. This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term “motifs” that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. Introduction The concept of rate transient analysis (RTA) involves the use of rate and pressure trends of producing wells to estimate properties such as permeability and fracture surface area. While very useful, RTA is an analytical technique and has commensurate limitations. In the complete paper, different RTA motifs are generated using a simulator. Insights from these motif simulations are used to modify simulation parameters to expediate and inform the history- matching process. The simulation history-matching work flow presented includes the following steps: 1 - Set up a simulation model with geologic properties, wellbore and completion designs, and fracturing and production schedules 2 - Run an initial model 3 - Tune the fracture geometries (height and length) to heuristic data: microseismic, frac-hit data, distributed acoustic sensing, or other diagnostics 4 - Match instantaneous shut-in pressure (ISIP) and wellhead pressure (WHP) during injection 5 - Make RTA plots of the real and simulated production data 6 - Use the motifs presented in the paper to identify possible production mechanisms in the real data 7 - Adjust history-matching parameters in the simulation model based on the intuition gained from RTA of the real data 8 -Iterate Steps 5 through 7 to obtain a match in RTA trends 9 - Modify relative permeabilities as necessary to obtain correct oil, water, and gas proportions In this study, the authors used a commercial simulator that fully integrates hydraulic fracturing, wellbore, and reservoir simulation into a single modeling code. Matching Fracturing Data The complete paper focuses on matching production data, assisted by RTA, not specifically on the matching of fracturing data such as injection pressure and fracture geometry (Steps 3 and 4). Nevertheless, for completeness, these steps are very briefly summarized in this section. Effective fracture toughness is the most-important factor in determining fracture length. Field diagnostics suggest considerable variability in effective fracture toughness and fracture length. Typical half-lengths are between 500 and 2,000 ft. Laboratory-derived values of fracture toughness yield longer fractures (propagation of 2,000 ft or more from the wellbore). Significantly larger values of fracture toughness are needed to explain the shorter fracture length and higher net pressure values that are often observed. The authors use a scale- dependent fracture-toughness parameter to increase toughness as the fracture grows. This allows the simulator to match injection pressure data while simultaneously limiting fracture length. This scale-dependent toughness scaling parameter is the most-important parameter in determining fracture size.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

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.

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