INSIM-FPT-3D: A Data-Driven Model for History Matching, Water-Breakthrough Prediction and Well-Connectivity Characterization in Three-Dimensional Reservoirs

2021 ◽  
Author(s):  
Hui Zhao ◽  
Wei Liu ◽  
Xiang Rao ◽  
Guanglong Sheng ◽  
Huazhou Andy Li ◽  
...  
Keyword(s):  
Simulation Model ◽  
History Matching ◽  
3D Model ◽  
Reference Model ◽  
Three Dimensional ◽  
Data Driven ◽  
Series Data ◽  
Reference Models ◽  
Water Breakthrough

Abstract The data-driven interwell simulation model (INSIM) has been recognized as an effective tool for history matching and interwell-connectivity characterization of waterflooding reservoirs. INSIM-FT-3D (FT: front tracking) was recently developed to upgrade the applicationdimension of INSIM series data-driven models from two-dimensional (2D) to three-dimensional (3D). However, INSIM-FT-3D cannot accurately infer the dynamic change of well-connectivity and predict well's bottom-hole pressure (BHP). The main purpose of this study intends to expand the capability of INSIM-FT-3D to empower for the assimilation of BHPs, the reliable prediction of water breakthrough and the characterization of dynamic interwell-connectivities. The default setting of well index (WI) in INSIM-FT-3D based on Peaceman's equation does not yield accurate BHP estimates. We derive a WI that can honor the BHPs of a reference model composed of a set of 1D connections. When history matching BHPs of a 3D reservoir, we show that the derived WI is a better initial guess than that obtained from Peaceman's equation. We also develop a flow-path-tracking (FPT) algorithm to calculate the dynamic interwell properties (allocation factors and pore volumes (PVs)). Besides, we discuss the relationship between the INSIM-family methods and the traditional grid-based methods, which indicates that the INSIM-family methods can calculate the transmissibility of the connection between coarse-scale cells in a more accurate manner. As an improvement of INSIM-FT-3D, the newly proposed data-driven model is denoted as INSIM-FPT-3D. To verify the correctness of the derived WI, we present a 1D problem and a T-shaped synthetic reservoir simulation model as the reference models. BHPs and oil production rates are obtained as the observed data by running these two reference models with total injection/production-rate controls. An INSIM-FPT-3D model is created by specifying the transmissibilities and PVs that are the same as those in the reference model. By applying the derived WIs in INSIM-FPT-3D, the resulting BHPs and oil rates obtained agree well with the reference model without further model calibration. Applying INSIM-FPT-3D to a synthetic multi-layered reservoir shows that we obtain a reasonable match of both BHPs and oil rates with INSIM-FPT-3D. Compared with the FrontSim model, the INSIM-FPT-3D model after history matching is shown to match the dynamic PVs from FrontSim reasonably well and can correctly predict the timing of water breakthrough. By allowing for the assimilation of BHP data, we enable INSIM-FPT-3D to history match a green field with limited production history and forecast the timing of water breakthrough. The improved INSIM-FPT-3D leads to more accurate characterization of the interwell connectivities.

scholarly journals Visualization of the Meridian System Based on Biomedical Information about Acupuncture Treatment

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
In-Seon Lee ◽  
Soon-Ho Lee ◽  
Song-Yi Kim ◽  
Hyejung Lee ◽  
Hi-Joon Park ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
3D Model ◽  
Acupuncture Treatment ◽  
Three Dimensional ◽  
Data Driven ◽  
Low Back ◽  
Treatment Regimens ◽  
Information Frequency ◽  
The Relationship

The origin of the concept of the meridian system is closely connected with the treatment effects of acupuncture, and it serves as an empirical reference system in the clinical setting. Understanding the meridian channels would be a first step in enhancing the clinical efficacy of acupuncture treatment. To understand the relationship between the location of the disease and the sites of relevant acupoints, we investigated acupuncture treatment regimens for low-back pain in 37 clinical studies. We found that the most frequently used acupoints in the treatment of low-back pain were BL23 (51%), BL25 (43%), BL24 (32%), BL40 (32%), BL60 (32%), GB30 (32%), BL26 (28%), BL32 (28%), and GB34 (21%). For the example of low-back pain, we visualized the biomedical information (frequency rates) about acupuncture treatment on the meridians of a three-dimensional (3D) model of the human body. We found that both local and distal acupoints were used to treat low-back pain in clinical trials based on the meridian theory. We suggest a new model for the visualization of a data-driven 3D meridian system of biomedical information about the meridians and acupoints. These findings may be helpful in understanding the meridian system and revealing the effectiveness of acupuncture treatment.

Accuracies of 3D printers with hard and soft materials

2020 ◽  
Vol 26 (7) ◽  
pp. 1227-1235 ◽  
Author(s):  
Taehun Kim ◽  
Guk Bae Kim ◽  
Hyun Kyung Song ◽  
Yoon Soo Kyung ◽  
Choung-Soo Kim ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Reference Model ◽  
Three Dimensional ◽  
Soft Materials ◽  
Hard Material ◽  
Content Type ◽  
Reference Models ◽  
Hard Materials ◽  
Jet Printing ◽  
Aided Design

Purpose This study aims to systemically evaluate morphological printing errors between computer-aided design (CAD) and reference models fabricated using two different three-dimensional printing (3DP) technologies with hard and soft materials. Design/methodology/approach The reference models were designed to ensure simpler and more accurate measurements than those obtained from actual kidney simulators. Three reference models, i.e. cube, dumbbell and simplified kidney, were manufactured using photopolymer jetting (PolyJet) with soft and hard materials and multi-jet printing (MJP) with hard materials. Each reference model was repeatably measured five times using digital calipers for each length. These values were compared with those obtained using CAD. Findings The results demonstrate that the cube models with the hard material of MJP and hard and soft materials of PolyJet were smaller (p = 0.022, 0.015 and 0.057, respectively). The dumbbell model with the hard material of MJP was smaller (p = 0.029) and that with the soft material of PolyJet was larger (p = 0.020). However, the dumbbell with the hard material of PolyJet generated low errors (p = 0.065). Finally, the simplified kidney models with the hard material of MJP and soft materials of PolyJet were smaller (p = 0.093 and 0.021) and that with the hard material of PolyJet was opposite to the former models (p = 0.043). Originality/value This study, to the best of authors’ knowledge, is the first to determine the accuracy between CAD and reference models fabricated using two different 3DP technologies with multi-materials. Thus, it serves references for surgical applications as simulators and guides that require accuracy.

A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs

SPE Journal ◽  
2018 ◽  
Vol 23 (04) ◽  
pp. 1105-1125 ◽  
Author(s):  
Yanbin Zhang ◽  
Jincong He ◽  
Changdong Yang ◽  
Jiang Xie ◽  
Robert Fitzmorris ◽  
...  
Keyword(s):  
History Matching ◽  
Unconventional Reservoirs ◽  
Data Driven ◽  
Linear Flow ◽  
Multiple Data ◽  
Straight Line ◽  
Diagnostic Characteristics ◽  
Ensemble Smoother ◽  
1D Numerical Simulation

Summary We developed a physics-based data-driven model for history matching, prediction, and characterization of unconventional reservoirs. It uses 1D numerical simulation to approximate 3D problems. The 1D simulation is formulated in a dimensionless space by introducing a new diffusive diagnostic function (DDF). For radial and linear flow, the DDF is shown analytically to be a straight line with a positive or zero slope. Without any assumption of flow regime, the DDF can be obtained in a data-driven manner by means of history matching using the ensemble smoother with multiple data assimilation (ES-MDA). The history-matched ensemble of DDFs offers diagnostic characteristics and probabilistic predictions for unconventional reservoirs.

scholarly journals Variation of physiological parameters in juvenile treetops of Eucalyptus tereticornis from a three-dimensional perspective

2018 ◽  
Vol 2 (23) ◽  
Author(s):  
Juan Carlos Valverde ◽  
Dagoberto Arias
Keyword(s):  
3D Model ◽  
Three Dimensional ◽  
Physiological Parameters ◽  
Eucalyptus Tereticornis ◽  
Physiological Variables

We analyzed the characterization of physiological variables in treetops of E. tereticornis; obtaining An of 28.6-40.6 μmol m-2s-1, E of 8.53-16.90 μmol m-2s-1, Gs of 87.47-335.16 mmol m-2s-1, Pp of 65-320 kPa and SPAD of 20.5-38.40; The analysis showed that external and medium branch in higher treetop obtained physiological behaviors significantly lower than the rest of treetop, because they are growing and leaves have not yet reached their peak. Also, we found that 3D model allowed the developed model that simplified information.

Evaluation of Structural and Biomechanical Characterization of Implant for Cranial Restoration

2019 ◽  
Vol 9 (7) ◽  
pp. 898-903
Author(s):  
Fu-Hui Tsai ◽  
Han-Yi Cheng
Keyword(s):  
Finite Element ◽  
3D Printing ◽  
3D Model ◽  
Three Dimensional ◽  
Biomechanical Properties ◽  
Clinical Applications ◽  
Finite Element Models ◽  
Critical Factors ◽  
Circular Structure

The objective this research was to investigate the biomechanical properties of various structures and thicknesses of implants for cranial restoration. A three-dimensional (3D) printing (3DP) technique has been applied in factories for several decades, but it was only recently introduced to the dental field less than 10 years ago. The structures of pre-shaped cranial mesh implants are critical factors for clinical applications. Many previous studies used finite element models to investigate for implants, but few examined a 3D model for pre-shaped cranial mesh implants with different structures and thicknesses. 3D cranial models were reconstructed using computer tomography to simulate preshaped cranial mesh implants under physical impacts. Data indicated that the stress significantly decreased when implants with greater thicknesses were used. Moreover, the implant with a circular structure created a relatively smaller stress that was approximately 7% lower compared to the implant with a triangular structure. As described above, the results of the present study demonstrate that 3DP-Ti is a reliable material of implants for cranial restoration.

Flow-Path Tracking Strategy in a Data-Driven Interwell Numerical Simulation Model for Waterflooding History Matching and Performance Prediction with Infill Wells

SPE Journal ◽  
2019 ◽  
Vol 25 (02) ◽  
pp. 1007-1025 ◽  
Author(s):  
Hui Zhao ◽  
Lingfei Xu ◽  
Zhenyu Guo ◽  
Qi Zhang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
History Matching ◽  
Flow Path ◽  
Path Tracking ◽  
Data Driven ◽  
Dynamic Allocation ◽  
Field Problem ◽  
Saturation Field ◽  
And Control

Summary Recently, we have developed two computationally efficient data-driven models—interwell numerical simulation model (INSIM) and INSIM with front tracking (INSIM-FT)—for history matching, prediction, characterization, and optimization of waterflooding reservoirs. Then, stemming from the INSIM family, we derived a new data-driven model referred to as INSIM with flow-path tracking (FPT), for more-accurate interwell connectivity calculations, dynamic flow-path tracking, and waterflooding predictions. The model is a connection-based simulation model that is developed on the basis of a two-phase-flow material-balance equation. With the new model, we can characterize a reservoir by history matching the historical well flow-rate data without the detailed petrophysical properties of the reservoir. In INSIM-FPT, we provide an automatic and systematic workflow that incorporates Delaunay triangulation and imaginary wells to construct the model connection map. We apply a modified depth-first searching method to track all influential flow paths between an injector/producer pair for more-accurate calculations of dynamic allocation factors and control pore volumes (PVs). In addition, we provide a method to visualize a saturation field for a history-matched INSIM-FPT model. On the basis of the saturation map, we design a workflow to evaluate possible drilling locations and future performance of infill wells. For application, we create a synthetic reservoir with two different scenarios to demonstrate the reliability of INSIM-FPT. The results show that the dynamic allocation factors and control PVs between injector/producer pairs in the history-matched INSIM-FPT models are consistent with those obtained from the true streamline simulation model. Furthermore, the oil-saturation field generated with INSIM-FPT reasonably matches that obtained with the true model. It shows that the future predictions of infill wells on the basis of history-matched INSIM-FPT models are reasonably accurate but can be improved if more observed data are collected from near the planned infill wells. We also test a large-scale field problem with 65 wells, which shows INSIM-FPT can reasonably match and predict the field data.

scholarly journals Molecular Cloning and Exploration of the Biochemical and Functional Analysis of Recombinant Glucose-6-Phosphate Dehydrogenase from Gluconoacetobacter diazotrophicus PAL5

2019 ◽  
Vol 20 (21) ◽  
pp. 5279 ◽  
Author(s):  
Edson Jiovany Ramírez-Nava ◽  
Daniel Ortega-Cuellar ◽  
Abigail González-Valdez ◽  
Rosa Angélica Castillo-Rodríguez ◽  
Gabriel Yaxal Ponce-Soto ◽  
...  
Keyword(s):  
3D Model ◽  
Three Dimensional ◽  
Nicotinamide Adenine Dinucleotide Phosphate ◽  
Endophytic Bacterium ◽  
Pentose Phosphate ◽  
Native State ◽  
E Coli ◽  
Optimal Activity ◽  
Glucose 6 Phosphate Dehydrogenase

Gluconacetobacter diazotrophicus PAL5 (GDI) is an endophytic bacterium with potential biotechnological applications in industry and agronomy. The recent description of its complete genome and its principal metabolic enzymes suggests that glucose metabolism is accomplished through the pentose phosphate pathway (PPP); however, the enzymes participating in this pathway have not yet been characterized in detail. The objective of the present work was to clone, purify, and biochemically and physicochemically characterize glucose-6-phosphate dehydrogenase (G6PD) from GDI. The gene was cloned and expressed as a tagged protein in E. coli to be purified by affinity chromatography. The native state of the G6PD protein in the solution was found to be a tetramer with optimal activity at pH 8.8 and a temperature between 37 and 50 °C. The apparent Km values for G6P and nicotinamide adenine dinucleotide phosphate (NADP+) were 63 and 7.2 μM, respectively. Finally, from the amino acid sequence a three-dimensional (3D) model was obtained, which allowed the arrangement of the amino acids involved in the catalytic activity, which are conserved (RIDHYLGKE, GxGGDLT, and EKPxG) with those of other species, to be identified. This characterization of the enzyme could help to identify new environmental conditions for the knowledge of the plant–microorganism interactions and a better use of GDI in new technological applications.

Bridging the Gap Between Material Balance and Reservoir Simulation for History Matching and Probabilistic Forecasting Using Machine Learning

2021 ◽  
Author(s):  
Nigel H. Goodwin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Simulation Model ◽  
Reservoir Simulation ◽  
History Matching ◽  
Material Balance ◽  
Data Driven ◽  
Probabilistic Forecasting ◽  
Reservoir Model ◽  
Reservoir Simulation Model

Abstract Objectives/Scope Methods for efficient probabilistic history matching and forecasting have been available for complex reservoir studies for nearly 20 years. These require a surprisingly small number of reservoir simulation runs (typically less than 200). Nowadays, the bottleneck for reservoir decision support is building and maintaining a reservoir simulation model. This paper describes an approach which does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance. It can be useful where a full simulation model is not economically justified, or where rapid decisions need to be made. Methods, Procedures, Process Previous work has described the use of proxy models and Hamiltonian Markov Chain Monte Carlo to produce valid probabilistic forecasts. To generate a data driven model, we take historical measurements of rates and pressures at each well, and apply multi-variate time series to generate a set of differential-algebraic equations (DAE) which can be integrated over time using a fully implicit solver. We combine the time series models with material balance equations, including a simple PVT and Z factor model. The parameters are adjusted in a fully Bayesian manner to generate an ensemble of models and a probabilistic forecast. The use of a DAE distinguishes the approach from normal time-series analysis, where an ARIMA model or state space model is used, and is normally only reliable for short term forecasting. Results, Observations, Conclusions We apply these techniques to the Volve reservoir model, and obtain a good history match. Moreover, the effort to build a reservoir model has been removed. We demonstrate the feasibility of simple physics models, and open up the possibility of combinations of physics models and machine learning models, so that the most appropriate approach can be used depending on resources and reservoir complexity. We have bridged the gap between pure machine learning models and full reservoir simulation. Novel/Additive Information The approach to use multi-variate time series analysis to generate a set of ordinary differential equations is novel. The extension of previously described probabilistic forecasting to a generalised model has many possible applications within and outside the oil and gas industry, and is not restricted to reservoir simulation.

Sign in / Sign up

Export Citation Format

Share Document