Exploiting Natural Nonconservative Geochemical Data to Obtain Reservoir Information

2021 ◽  
Author(s):  
Yanqing Wang ◽  
Zhe Liu ◽  
Yuchen Zhang ◽  
Jun Lu
Keyword(s):  
Production Rate ◽  
History Matching ◽  
Produced Water ◽  
Clay Content ◽  
Parameters Estimation ◽  
Reservoir Rock ◽  
Chemical Data ◽  
Geochemical Data ◽  
Model Parameters ◽  
Concentration Data

Abstract Geochemical data in produced water contain important reservoir information but are seldomly exploited, especially for the nonconservative chemicals. Some conservative chemical data have been integrated in history matching workflow to obtain better knowledge of reservoirs. However, assuming reservoir chemicals being conservative is impractical because most chemicals are involved in interactions with other chemicals or reservoir rock, and mistakenly regarding nonconservative chemicals as being conservative can cause large error. Nevertheless, once the interactions can be accurately described, nonconservative chemical data can be used to obtain more reservoir information. In this work, a new physicochemical model is proposed to describe the transport of natural nonconservative chemicals (barium and sulfate) in porous media. Both physical reactions, such as ion adsorption and desorption, and chemical reactions, such as barite deposition, are integrated. Based on the new model, the ensemble smoother with multiple data assimilations (ES-MDA) method is employed to update reservoir model parameters by assimilating oil production rate, water production rate, and chemical data (barium and sulfate concentration). Data assimilation results show that integrating geochemical data in ES-MDA algorithm yields additional improvements in estimation of permeability. Besides, clay content distribution, which is critical in injection water breakthrough percentage calculation, can be accurately estimated with relative root mean square error (rRMSE) being as small as 0.1. However, mistakenly regarding nonconservative chemicals as conservative can cause large errors in reservoir parameters estimation. Accurately modeling the chemical interactions is crucial for integrating chemical data in history matching algorithm.

Seawater Breakthrough Monitoring and Reservoir-Model Improvement Using Natural Boron

2021 ◽  
Author(s):  
Yanqing Wang ◽  
Zhe Liu ◽  
Xiang Li ◽  
Shiqian Xu ◽  
Jun Lu
Keyword(s):  
Production Rate ◽  
History Matching ◽  
Produced Water ◽  
Clay Content ◽  
Ion Concentration ◽  
Geochemical Data ◽  
Reservoir Model ◽  
Concentration Data ◽  
Water Breakthrough ◽  
Reservoir Simulator

Abstract Natural geochemical data, which refer to the natural ion concentration in produced water, contain important reservoir information, but is seldomly exploited. Some ions were used as conservative tracers to obtain better knowledge of reservoir. However, using only conservative ions can limit the application of geochemical data as most ions are nonconservative and can either interact with formation rock or react with other ions. Besides, mistakenly using nonconservative ion as being conservative may cause unexpected results. In order to further explore the nonconservative natural geochemical information, the interaction between ion and rock matrix is integrated into the reservoir simulator to describe the nonconservative ion transport in porous media. Boron, which is a promising nonconservative ion, is used to demonstrate the application of nonconservative ion. Based on the new model, the boron concentration data together with water production rate and oil production rate are assimilated through ensemble smoother multiple data assimilation (ES-MDA) algorithm to improve the reservoir model. Results indicate that including nonconservative ion data in the history matching process not only yield additional improvement in permeability field, but also can predict the distribution of clay content, which can promote the accuracy of using boron data to determine injection water breakthrough percentage. However, mistakenly regarding nonconservative ion being conservative in the history matching workflow can deteriorate the accuracy of reservoir model.

Parameters Estimation for Chemical Flooding Modeling Using Ensemble-Based Method

2021 ◽  
Author(s):  
Xindan Wang ◽  
Yin Zhang ◽  
Abhijit Dandekar ◽  
Yudou Wang
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
History Matching ◽  
Simulation Models ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Chemical Flooding ◽  
Multiple Model ◽  
Assisted History Matching ◽  
Matching Techniques

Abstract Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.

Produced-Water-Chemistry History Matching in the Janice Field

2015 ◽  
Vol 18 (04) ◽  
pp. 564-576 ◽  
Author(s):  
O.. Vazquez ◽  
C.. Young ◽  
V.. Demyanov ◽  
D.. Arnold ◽  
A.. Fisher ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Production Rate ◽  
History Matching ◽  
Produced Water ◽  
Chloride Concentration ◽  
Pso Algorithm ◽  
Sources Of Information ◽  
Water Production Rate ◽  
Oilfield Operations ◽  
Complex Faulting

Summary Produced-water-chemistry (PWC) data are the main sources of information to monitor scale precipitation in oilfield operations. Chloride concentration is used to evaluate the seawater fraction of the total produced water per producing well and is included as an extra history-matching constraint to reevaluate a good conventionally history-matched (HM) reservoir model for the Janice field. Generally, PWC is not included in conventional history matching, and this approach shows the value of considering the nature of the seawater-injection front and the associated brine mixing between the distinctive formation water and injected seawater. Adding the extra constraint resulted in the reconceptualization of the reservoir geology between a key injector and two producers. The transmissibility of a shale layer is locally modified within a range of geologically consistent values. Also, a major lineament is identified which is interpreted as a northwest/southeast-trending fault, whereby the zero transmissibility of a secondary shale in the Middle Fulmar is locally adjusted to allow crossflow. Both uncertainties are consistent with the complex faulting known to exist in the region of the targeted wells. Other uncertainties that were carried forward to the assisted-history-matching phase included water allocation to the major seawater injectors; thermal fracture orientation of injectors; and the vertical and horizontal permeability ratio (Kv/Kh) of the Fulmar formation. Finally, a stochastic particle-swarm-optimization (PSO) algorithm is used to generate an ensemble of HM models with seawater fraction as an extra constraint in the misfit definition. Use of additional data in history matching has improved the original good HM solution. Field oil-production rate is interpreted as improved over a key period, and although no obvious improvement was observed in field water-production rate, seawater fraction in a number of wells was improved.

scholarly journals DEVELOPMENT OF PRODUCTION RATE DECLINE-BASED SOFTWARE FOR RESERVOIR PERFORMANCE PREDICTION

2019 ◽  
Vol 7 (6) ◽  
pp. 49-55
Author(s):  
Innocent O. Oboh ◽  
Anietie N. Okon ◽  
Hocaha F. Enyi
Keyword(s):  
Production Rate ◽  
Performance Prediction ◽  
History Matching ◽  
Petroleum Industry ◽  
Parameters Estimation ◽  
The Other ◽  
Reservoir Engineering ◽  
Reservoir Performance ◽  
Performance Predictions ◽  
Inflow Performance

The need for predicting the performance of hydrocarbon reservoirs has led to the development of a number of software in the Petroleum industry. Lots of these available software handled virtually all tasks in reservoir engineering ranging from estimation, forecasting, history matching, among others. That notwithstanding, improvements on these software are still been made and newer versions are released to meet users’ requirements. In this work, software “ULTIMATE” was developed based on production rate decline analysis to predict reservoir performance. Also, the developed software “ULTIMATE” handles Inflow Performance Relationship (IPR) prediction. The developed software prediction based on data obtained from two wells were compared with another software MBAL 10.5 developed by petroleum Experts Limited. The results of the comparison indicated that the developed ULTIMATE software predictions were very close to the MBAL 10.5 predictions. Additionally, the incorporated parallel algorithm in the ULTIMATE software enables it to analyze more wells and with more speed than the other software (MBAL 10.5). Therefore, the developed ULTIMATE software can be use as quick tool for predicting reservoir performance based on production rate decline analysis. Furthermore, the developed software would be improved to handle reservoir performance predictions based on Materials Balance Equation (MBE), and other production rate-related predictions like coning parameters estimation.

scholarly journals Data workflow for generating static geological model platform for petroleum prospect re-evaluation of Wytch Farm field, Wessex Basin, South Coast, United Kingdom

2020 ◽  
Vol 50 (3) ◽  
pp. 347-376
Author(s):  
Olawale Olakunle OSINOWO
Keyword(s):  
United Kingdom ◽  
Production Rate ◽  
History Matching ◽  
Relevant Information ◽  
South Coast ◽  
Reservoir Rock ◽  
Geological Model ◽  
Daily Production ◽  
Water Contact ◽  
Model Platform

The analyses and interpretation of the old existing 3D seismic and well log data of Wytch Farm field, Wessex Basin, south coast of the United Kingdom provided relevant information employed to generate static geological model platform. The platform which is requisite for building and distributing petrophysical and production data across the field could be useful for simulating the field's performance, history matching as well as generate future prediction/forecast needful to establish the reasons for the early water breakthrough and sudden production decline from a peak value of 115,000 bbl/day in 1997 to the present production rate of less than 19,000 bbl/day. Six major faults which extend throughout the entire length of the field and five horizon surfaces which correspond to the top of Greensand, Combrash, Mercia mudstone and the top and base of Sherwood Formations aided the generation of the static geological model platform across the Wytch Farm field. The volume of delineated saturated reservoir rock lying above the Oil Water Contact (OWC) and enclosed within fault assisted anticlinal structures indicate a Gross Rock Volume (GRV) of 2,007,000,000 m3 and thus suggest that Wytch Farm field may likely still hold hydrocarbon potential beyond the present daily production rate. This potential could be unlocked through adequate reservoir management that requires detailed information such as could be generated from static geological model of the field.

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

scholarly journals Formulating the history matching problem with consistent error statistics

2021 ◽  
Author(s):  
Geir Evensen
Keyword(s):  
Simulation Model ◽  
Measurement Errors ◽  
History Matching ◽  
Critical Role ◽  
Bayes Theorem ◽  
Conditional Probability Density ◽  
Model Parameters ◽  
Rate Data ◽  
Matching Problem ◽  
Reservoir Prediction

AbstractIt is common to formulate the history-matching problem using Bayes’ theorem. From Bayes’, the conditional probability density function (pdf) of the uncertain model parameters is proportional to the prior pdf of the model parameters, multiplied by the likelihood of the measurements. The static model parameters are random variables characterizing the reservoir model while the observations include, e.g., historical rates of oil, gas, and water produced from the wells. The reservoir prediction model is assumed perfect, and there are no errors besides those in the static parameters. However, this formulation is flawed. The historical rate data only approximately represent the real production of the reservoir and contain errors. History-matching methods usually take these errors into account in the conditioning but neglect them when forcing the simulation model by the observed rates during the historical integration. Thus, the model prediction depends on some of the same data used in the conditioning. The paper presents a formulation of Bayes’ theorem that considers the data dependency of the simulation model. In the new formulation, one must update both the poorly known model parameters and the rate-data errors. The result is an improved posterior ensemble of prediction models that better cover the observations with more substantial and realistic uncertainty. The implementation accounts correctly for correlated measurement errors and demonstrates the critical role of these correlations in reducing the update’s magnitude. The paper also shows the consistency of the subspace inversion scheme by Evensen (Ocean Dyn. 54, 539–560 2004) in the case with correlated measurement errors and demonstrates its accuracy when using a “larger” ensemble of perturbations to represent the measurement error covariance matrix.

Produced-Water-Chemistry History Matching Using a 1D Reactive Injector/Producer Reservoir Model

2013 ◽  
Vol 28 (04) ◽  
pp. 369-375 ◽  
Author(s):  
Oscar Vazquez ◽  
Ross A. McCartney ◽  
Eric Mackay
Keyword(s):  
Water Chemistry ◽  
History Matching ◽  
Produced Water ◽  
Reservoir Model

Wettability Alteration to Intermediate Gas-Wetting in Gas-Condensate Reservoirs at High Temperatures

SPE Journal ◽  
2007 ◽  
Vol 12 (04) ◽  
pp. 397-407 ◽  
Author(s):  
Mashhad Mousa Fahes ◽  
Abbas Firoozabadi
Keyword(s):  
Hydraulic Fracturing ◽  
Production Rate ◽  
High Temperatures ◽  
Gas Production ◽  
Reservoir Rock ◽  
Wettability Alteration ◽  
Absolute Permeability ◽  
Low Permeability ◽  
Gas Well ◽  
Water Blocking

Summary Wettability of two types of sandstone cores, Berea (permeability on the order of 600 md), and a reservoir rock (permeability on the order of 10 md), is altered from liquid-wetting to intermediate gas-wetting at a high temperature of 140C. Previous work on wettability alteration to intermediate gas-wetting has been limited to 90C. In this work, chemicals previously used at 90C for wettability alteration are found to be ineffective at 140C. New chemicals are used which alter wettability at high temperatures. The results show that:wettability could be permanently altered from liquid-wetting to intermediate gas-wetting at high reservoir temperatures,wettability alteration has a substantial effect on increasing liquid mobility at reservoir conditions,wettability alteration results in improved gas productivity, andwettability alteration does not have a measurable effect on the absolute permeability of the rock for some chemicals. We also find the reservoir rock, unlike Berea, is not strongly water-wet in the gas/water/rock system. Introduction A sharp reduction in gas well deliverability is often observed in many low-permeability gas-condensate reservoirs even at very high reservoir pressure. The decrease in well deliverability is attributed to condensate accumulation (Hinchman and Barree 1985; Afidick et al. 1994) and water blocking (Engineer 1985; Cimolai et al. 1983). As the pressure drops below the dewpoint, liquid accumulates around the wellbore in high saturations, reducing gas relative permeability (Barnum et al. 1995; El-Banbi et al. 2000); the result is a decrease in the gas production rate. Several techniques have been used to increase gas well deliverability after the initial decline. Hydraulic fracturing is used to increase absolute permeability (Haimson and Fairhurst 1969). Solvent injection is implemented in order to remove the accumulated liquid (Al-Anazi et al. 2005). Gas deliverability often increases after the reduction of the condensate saturation around the wellbore. In a successful methanol treatment in Hatter's Pond field in Alabama (Al-Anazi et al. 2005), after the initial decline in well deliverability by a factor of three to five owing to condensate blocking, gas deliverability increased by a factor of two after the removal of water and condensate liquids from the near-wellbore region. The increased rates were, however, sustained for a period of 4 months only. The approach is not a permanent solution to the problem, because the condensate bank will form again. On the other hand, when hydraulic fracturing is used by injecting aqueous fluids, the cleanup of water accumulation from the formation after fracturing is essential to obtain an increased productivity. Water is removed in two phases: immiscible displacement by gas, followed by vaporization by the expanding gas flow (Mahadevan and Sharma 2003). Because of the low permeability and the wettability characteristics, it may take a long time to perform the cleanup; in some cases, as little as 10 to 15% of the water load could be recovered (Mahadevan and Sharma 2003; Penny et al. 1983). Even when the problem of water blocking is not significant, the accumulation of condensate around the fracture face when the pressure falls below dewpoint pressure could result in a reduction in the gas production rate (Economides et al. 1989; Sognesand 1991; Baig et al. 2005).

Sign in / Sign up

Export Citation Format

Share Document