scholarly journals Production Splitting Method for Commingled Production Reservoir Based on Automatic History Matching of Single Well

Lithosphere ◽  
2022 ◽  
Vol 2022 (Special 1) ◽  
Author(s):  
Yingfei Sui ◽  
Chuanzhi Cui ◽  
Zhen Wang ◽  
Yong Yang ◽  
Peifeng Jia
Keyword(s):  
Numerical Simulation ◽  
History Matching ◽  
Splitting Method ◽  
Production Performance ◽  
Water Flooding ◽  
Production Model ◽  
Mitigation Measures ◽  
Automatic History Matching ◽  
Well Production ◽  
Single Well

Abstract The interlayer interference is very serious in the process of water flooding development, especially when the reservoir adopts commingling production. The implementation of various interlayer interference mitigation measures requires that the production performance parameters and remaining oil distribution of each layer of the reservoir should be clearly defined, and the accurate production splitting of oil wells is the key. In this paper, the five-spot pattern is simplified to a single well production model of commingled production centered on oil well. The accurate production splitting results are obtained through automatic history matching of single well production performance. The comparison between the calculation results of this method and that of reservoir numerical simulation shows that the method is simple, accurate, and practical. In the field application, for the multilayer commingled production reservoir without accurate numerical simulation, this method can quickly and accurately realize the production splitting of the reservoir according to the development performance data.

A Cognitive Data-Driven Single-Well Modeling Workflow for Reservoir Deliverability Predictions – Expanding the Wireline Formation Tester Application Envelope

2021 ◽  
Author(s):  
Abdul Bari ◽  
Mohammad Rasheed Khan ◽  
M. Sohaib Tanveer ◽  
Muhammad Hammad ◽  
Asad Mumtaz Adhami ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Production Performance ◽  
Data Driven ◽  
Analytical Models ◽  
Production Model ◽  
Reservoir Evaluation ◽  
Fluid Properties ◽  
Petrophysical Model ◽  
Single Well ◽  
Data Driven Approach

Abstract In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.

scholarly journals History Matching and Production Prediction of Steam Drive Reservoir Based on Data-Space Inversion Method

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
RuXiang Gong ◽  
JingSong Li ◽  
ZiJun Huang ◽  
Fei Wang ◽  
Hao Yang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
History Matching ◽  
Production Optimization ◽  
Production Performance ◽  
Inversion Method ◽  
Data Space ◽  
Steam Flooding ◽  
Space Inversion ◽  
Production Prediction

Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability.

A New Artificial Intelligence Method to Predict Water Flooding Performance in Layered Reservoir

2021 ◽  
Author(s):  
Cunliang Chen ◽  
Xiaodong Han ◽  
Wei Zhang ◽  
Yanhui Zhang ◽  
Fengjun Zhou
Keyword(s):  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Computing Time ◽  
Vertical Direction ◽  
Single Layer ◽  
Oil Production ◽  
Oil Field ◽  
Production Performance ◽  
New Method ◽  
Water Flooding

Abstract The ultimate goal of oilfield development is to maximize the investment benefits. The reservoir performance prediction is directly related to oilfield investment and management. The traditional strategy based on numerical simulation has been widely used with the disadvantages of long run time and much information needed. It is necessary to form a fast and convenient method for the oil production prediction, especially for layered reservoir. A new method is proposed to predict the development indexes of multi-layer reservoirs based on the injection-production data. The new method maintains the objectivity of the data and demonstrates the superiority of the intelligent algorithm. The layered reservoir is regarded as a series of single layer reservoirs on the vertical direction. Considering the starting pressure gradient of non-Newtonian fluid flow and the variation of water content in the oil production index, the injection-production response model for single-layer reservoirs is established. Based on that, a composite model for the multi-layer reservoir is established. For model solution, particle swarm optimization is applied for optimization of the new model. A heterogeneous multi-layer model was established for validation of the new method. The results obtained from the new proposed model are in consistent with the numerical simulation results. It saves a lot of computing time with the incorporation of the artificial intelligence methods. It showed that this technique is valid and effective to predict oil performance in layered reservoir. These examples showed that the application of big data and artificial intelligence method is of great significance, which not only shortens the working time, but also obtains relatively higher accuracy. Based on the objective data of the oil field and the artificial intelligence algorithm, the prediction of oil field development data can be realized. This technique has been used in nearly 100 wells of Bohai oilfields. The results showed in this paper reveals that it is possible to estimate the production performance of the water flooding reservoirs.

Simultaneous History-Matching Approach by Use of Reservoir-Characterization and Reservoir-Simulation Studies

2016 ◽  
Vol 19 (04) ◽  
pp. 694-712 ◽  
Author(s):  
Guilherme Daniel Avansi ◽  
Célio Maschio ◽  
Denis José Schiozer
Keyword(s):  
Numerical Simulation ◽  
History Matching ◽  
Reservoir Characterization ◽  
Well Log ◽  
Log Data ◽  
Field Development ◽  
Geostatistical Methods ◽  
Well Production ◽  
Matching Process ◽  
Geological Models

Summary Reservoir characterization is the key to success in history matching and production forecasting. Thus, numerical simulation becomes a powerful tool to achieve a reliable model by quantifying the effect of uncertainties in field development and management planning, calibrating a model with history data, and forecasting field production. History matching is integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D-seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated-history-matching studies use a unique objective function (OF), this is not enough. History matching by simultaneous calibrations of different OFs is necessary because all OFs must be within the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization, applying a simultaneous calibration of different OFs in a history-matching procedure, and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels, avoiding the geological discontinuities, to match the reservoir numerical model. The proposed methodology comprises a geostatistical method to model the spatial reservoir-property distribution on the basis of the well-log data; numerical simulation; and adjusting conditional realizations (models) on the basis of geological modeling (variogram model, vertical-proportion curve, and regularized well-log data). In addition, reservoir uncertainties are included, simultaneously adjusting different OFs to evaluate the history-matching process and virtual wells to perturb geological continuities. This methodology effectively preserves the consistency of geological models during the history-matching process. We also simultaneously combine different OFs to calibrate and validate the models with well-production data. Reliable numerical and geological models are used in forecasting production under uncertainties to validate the integrated procedure.

Numerical Simulation of Well Production Performance Considering Pre-Darcy Flow Using EDFM

2018 ◽  
Author(s):  
Jianchun Xu ◽  
Baojiang Sun ◽  
Wei Zhang ◽  
Hongjie Cheng ◽  
Weiqi Fu
Keyword(s):  
Numerical Simulation ◽  
Production Performance ◽  
Darcy Flow ◽  
Well Production

scholarly journals An Automatic History Matching Method of Reservoir Numerical Simulation Based on Improved Genetic Algorithm

2012 ◽  
Vol 29 ◽  
pp. 3924-3928 ◽  
Author(s):  
Xian-song Zhang ◽  
Jian Hou ◽  
Dai-gang Wang ◽  
Ting-hua Mu ◽  
Jin-tao Wu ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
History Matching ◽  
Improved Genetic Algorithm ◽  
Matching Method ◽  
Automatic History Matching ◽  
Simulation Based

scholarly journals A New Growth Curve for Predicting Production Performance of Water-Flooding Oilfields

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Feng Liu ◽  
Yancheng Liu ◽  
Xiao Guo ◽  
Fei Yang ◽  
Ahuan Zhou ◽  
...  
Keyword(s):  
Growth Curve ◽  
Growth Curves ◽  
Oil Production ◽  
Production Performance ◽  
Water Flooding ◽  
Production Data ◽  
New Model ◽  
Well Production ◽  
Curve Method ◽  
Water Cut

Oil production and water cut prediction is one of the most important research contents of reservoir production performance analysis. The growth curve method has the advantages of the general water drive curve method and the combined solution model method with fewer parameters and simple and fast calculation process and so it has been widely used in well production prediction. Based on the analysis of 4W and 4Y4 model growth curves, a new generalized growth curve of the well production performance is proposed. The new model can forecast cumulative oil production, annual oil production, and water cut at different oilfield development periods. A MATLAB program was developed to derive the parameters in the new model. The built model was applied to the production data of the Samattalol oilfield and Daqing oilfield. The predicted cumulative oil production, annual oil production, and water cut are all close to the actual production data, and satisfactory results are obtained, which demonstrates the practicability and reliability of the new model.

Identification and Impacts of Dual Strings Completion Leak in History Matching of Highly Faulted and Multi Layered Clastic Oil Reservoirs

2021 ◽  
Author(s):  
Nis Ilyani Mohmad ◽  
Danu Ismadi ◽  
Nor Hajjar Salleh ◽  
Amirul Nur Romle ◽  
Syarifah Puteh Syed Abd Rahim ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Oil Recovery ◽  
History Matching ◽  
Production Performance ◽  
Model Parameters ◽  
Reservoir Model ◽  
Reservoir Performance ◽  
Tool Evaluation ◽  
Logging Tool

Abstract History matching is one of the paramount steps in reservoir model validation to describe, analyze and mimic the overall behavior of reservoir performance. Performing history matching on highly faulted and multi layered reservoirs is always challenging, especially when the wells are completed with multiple zones either with single selective or dual strings. The history matching complexity is also compounded with uncertainties in production allocation, well history and downhole equipment integrity overtime. It is a common practice for deterministic history matching in reservoir numerical simulation to modify the both static and dynamic model parameters within the subsurface uncertainty window. However, for multi layered reservoirs completed with dual strings, another parameter that is most often get overlooked is the completion string’s leaking phenomenon that tremendously impacting the history matching. The objective of this paper is to introduce dual strings leaking diagnostics methodology from various disciplines’ angles. We demonstrate these dual strings leaking phenomenon impact on history matching. This paper covers dual strings leak diagnostic methodology which includes production logging tool evaluation, well’s production performance and recovery factor analysis. Possible factors that gives rise to the string’s leaks including material corrosion from high CO2 and sand production will also be discussed. We will demonstrate on how the leak phenomenon could be mimicked in the reservoir numerical model. Possible risks on future infill well identification if the leaks phenomenon is not incorporated will be also discussed. The dual strings leaks diagnosis and application in numerical simulation is illustrated on a case study of Field "D", a multilayered sandstone reservoir in Malaysia of almost 3 decades of production. This proven leak identification and reservoir model history matching methodology has been replicated for all the fault blocks across the field. It potentially unlocks more than 100 MMSTB of additional oil recovery by drilling more oil producers and water injectors in future drilling campaigns.

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