Analysis of Production Data from Communicating Multifractured Horizontal Wells Using the Dynamic Drainage Area Concept

2021 ◽  
pp. 1-19
Author(s):  
Hossein Ahmadi ◽  
Christopher R. Clarkson ◽  
Hamidreza Hamdi ◽  
Hamid Behmanesh
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
Hydraulic Fracture ◽  
History Matching ◽  
Horizontal Wells ◽  
Drainage Area ◽  
Production Data ◽  
Reservoir Properties ◽  
Region Model ◽  
Fluid Transfer

Summary Reduction of fracture/well spacing and increases in hydraulic fracture stimulation treatment size are popular strategies for improving hydrocarbon recovery from multifractured horizontal wells (MFHWs). However, these strategies can also increase the chance of fracture interference, which can not only negatively impact the overall production but also introduce complexities for production data analysis. A semianalytical model is therefore developed to analyze production data from two communicating MFHWs and applied to a field case. The new semianalytical model uses the dynamic drainage area (DDA) concept and assumes three porosity regions. The three-region model is comprised of a primary hydraulic fracture (PHF), an enhanced fractured region (EFR) adjacent to the PHF, and a nonstimulated region (NSR). Assuming a well pair primarily communicates through PHFs, the equations for two communicating wells are coupled and solved simultaneously to model the fluid transfer between the wells. This method is used within a history-matching framework to estimate the communication between the wells by matching the production data. The semianalytical model is first verified against a more rigorous, fully numerical simulation model for a range of fracture/reservoir properties. These comparisons demonstrate that there is excellent agreement between the fully numerical simulation model results and the new semianalytical model. The semianalytical model is then employed to history-match production data from six MFHWs (drilled from two adjacent well pads) exhibiting different degrees of communication. For the purpose of history matching the data, only strong communication between pairs of wells (intrapair communication) is considered in the three-region model, and the results show good agreement with the field data. A flexible, yet simple, semianalytical model is developed for the first time that can accurately model the communication between multiple well pairs. This approach can be used by reservoir engineers to analyze the production data from communicating MFHWs.

History Matching and Rate Forecasting in Unconventional Oil Reservoirs With an Approximate Analytical Solution to the Double-Porosity Model

2015 ◽  
Vol 19 (01) ◽  
pp. 070-082 ◽  
Author(s):  
B. A. Ogunyomi ◽  
T. W. Patzek ◽  
L. W. Lake ◽  
C. S. Kabir
Keyword(s):  
Analytical Solution ◽  
History Matching ◽  
Horizontal Wells ◽  
Double Porosity ◽  
Unconventional Reservoirs ◽  
Production Data ◽  
Time Space ◽  
Double Porosity Model ◽  
Fractured Horizontal Wells ◽  
Porosity Model

Summary Production data from most fractured horizontal wells in gas and liquid-rich unconventional reservoirs plot as straight lines with a one-half slope on a log-log plot of rate vs. time. This production signature (half-slope) is identical to that expected from a 1D linear flow from reservoir matrix to the fracture face, when production occurs at constant bottomhole pressure. In addition, microseismic data obtained around these fractured wells suggest that an area of enhanced permeability is developed around the horizontal well, and outside this region is an undisturbed part of the reservoir with low permeability. On the basis of these observations, geoscientists have, in general, adopted the conceptual double-porosity model in modeling production from fractured horizontal wells in unconventional reservoirs. The analytical solution to this mathematical model exists in Laplace space, but it cannot be inverted back to real-time space without use of a numerical inversion algorithm. We present a new approximate analytical solution to the double-porosity model in real-time space and its use in modeling and forecasting production from unconventional oil reservoirs. The first step in developing the approximate solution was to convert the systems of partial-differential equations (PDEs) for the double-porosity model into a system of ordinary-differential equations (ODEs). After which, we developed a function that gives the relationship between the average pressures in the high- and the low-permeability regions. With this relationship, the system of ODEs was solved and used to obtain a rate/time function that one can use to predict oil production from unconventional reservoirs. The approximate solution was validated with numerical reservoir simulation. We then performed a sensitivity analysis on the model parameters to understand how the model behaves. After the model was validated and tested, we applied it to field-production data by partially history matching and forecasting the expected ultimate recovery (EUR). The rate/time function fits production data and also yields realistic estimates of ultimate oil recovery. We also investigated the existence of any correlation between the model-derived parameters and available reservoir and well-completion parameters.

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.

scholarly journals Building of Turbiditic Gas Field Dynamic Model with a Simplified 3D Simulation Software

2018 ◽  
Vol 3 (3) ◽  
pp. 141
Author(s):  
Octria Adi Prasojo ◽  
Vladimir Alejandro Choque Flores ◽  
Matthieu Plantevin ◽  
Reza Syahputra
Keyword(s):  
Simulation Model ◽  
History Matching ◽  
Structural Model ◽  
Simulation Software ◽  
3D Simulation ◽  
Well Testing ◽  
Data Quality Control ◽  
Reservoir Properties ◽  
Gas Field ◽  
3D Simulation Model

This study provides a novel approach of building 3D simulation model with extremely shorter time needed using Rubis simulation software from Kappa Engineering. The study focused on X Field that is located in a turbiditic setting, mainly consisted of separated channel bodies filled with gas, located in a slope apron or passive continental margin of Mahakam Delta. Methods of the study is quite contradictive with common reservoir simulation where it includes data integration, data quality control, model geometry building, reservoir properties distribution, and is followed by wells definition to build the 3D simulation model. Afterward, the reliability of the structural model was checked by the volume calculation for each segment from GeoX model where all dynamic and static data used in the simulation were checked using history matching data derived from well-testing. In conclusion, simulation was run and X Field will be producing for 23 years with 3 years and 10 months plateau rate. Where the static and dynamic data are already provided, the simulation conducted here was very beneficial during the exploration phase of a gas field where the whole process of modeling and simulation could be done only for 3 to 6 months.

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.

History Matching and Forecasting Tight Gas Condensate and Oil Wells by Use of an Approximate Semianalytical Model Derived From the Dynamic-Drainage-Area Concept

2016 ◽  
Vol 19 (04) ◽  
pp. 540-552 ◽  
Author(s):  
C. R. Clarkson ◽  
F.. Qanbari
Keyword(s):  
Numerical Simulation ◽  
History Matching ◽  
Transient Flow ◽  
Saturation Pressure ◽  
Gas Condensate ◽  
Drainage Area ◽  
Phase Flow ◽  
Tight Gas ◽  
Linear Flow ◽  
Wide Range

Summary Recently, low-permeability (tight) gas condensate and oil reservoirs have been the focus of exploitation by operators in North America. Multifractured horizontal wells (MFHWs) producing from these reservoirs commonly exhibit long periods of transient flow, during which two-phase flow of oil and gas begins because of well flowing pressures dropping to less than saturation pressure. History matching and forecasting of such wells can be rigorously performed by use of numerical simulation, but this approach requires significant data and time to set up. Analytical methods, although requiring fewer data and less time to apply, have historically been developed only for single-phase-flow scenarios. In this work, a novel and rigorous analytical method is developed for history matching and forecasting MFHWs experiencing multiphase flow during the transient and boundary-dominated flow periods. The distance-of-investigation (DOI) concept has been used for many years in pressure-transient analysis to estimate distances of reservoir boundaries to wells, among other applications. In the current work, the DOI concept is used to estimate dynamic drainage area (DDA) to forecast tight gas condensate and oil wells; a linear flow geometry is assumed. During transient flow, the DDA is calculated at each timestep by use of the linear-flow DOI formulation; a multiphase version of the linear-flow productivity-index (PI) equation and material-balance equations for gas, condensate, and oil are solved iteratively for pressure, saturation, and fluid-production rate. The PI equations for gas and oil use pseudopressure, which is evaluated with saturation/pressure relationships derived from pressure/volume/temperature data. For boundary-dominated flow, when the drainage area is static, the inflow equations are again coupled with material balance for both phases. The new method is validated against numerical simulation, covering a wide range of fluid properties and operating conditions. The new method matches the simulation acceptably for all cases studied. Field examples of MFHWs are also analyzed to demonstrate the practical applicability of the approach. The three liquid-rich shale examples analyzed were also chosen to represent a wide range of fluid properties. In all cases, acceptable history matches are achieved. The new analytical forecasting/history-matching procedure developed in this work provides a practical alternative to numerical simulation for tight gas condensate and oil experiencing two-phase flow during the transient-flow period. The method, which does not rely on Laplace-space solutions, is conceptually simple to understand, easy to implement, and avoids the inconvenience of Laplace-space inversion.

History Matching Using the Ensemble Kalman Filter With Multiscale Parameterization: A Field Case Study

SPE Journal ◽  
2010 ◽  
Vol 16 (02) ◽  
pp. 307-317 ◽  
Author(s):  
Yanfen Zhang ◽  
Dean S. Oliver
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Multiple Scales ◽  
Gaussian Random Fields ◽  
Rock Properties ◽  
Real Field ◽  
Production Data ◽  
Reservoir Properties ◽  
Water Cut

Summary The increased use of optimization in reservoir management has placed greater demands on the application of history matching to produce models that not only reproduce the historical production behavior but also preserve geological realism and quantify forecast uncertainty. Geological complexity and limited access to the subsurface typically result in a large uncertainty in reservoir properties and forecasts. However, there is a systematic tendency to underestimate such uncertainty, especially when rock properties are modeled using Gaussian random fields. In this paper, we address one important source of uncertainty: the uncertainty in regional trends by introducing stochastic trend coefficients. The multiscale parameters including trend coefficients and heterogeneities can be estimated using the ensemble Kalman filter (EnKF) for history matching. Multiscale heterogeneities are often important, especially in deepwater reservoirs, but are generally poorly represented in history matching. In this paper, we describe a method for representing and updating multiple scales of heterogeneity in the EnKF. We tested our method for updating these variables using production data from a deepwater field whose reservoir model has more than 200,000 unknown parameters. The match of reservoir simulator forecasts to real field data using a standard application of EnKF had not been entirely satisfactory because it was difficult to match the water cut of a main producer in the reservoir. None of the realizations of the reservoir exhibited water breakthrough using the standard parameterization method. By adding uncertainty in large-scale trends of reservoir properties, the ability to match the water cut and other production data was improved substantially. The results indicate that an improvement in the generation of the initial ensemble and in the variables describing the property fields gives an improved history match with plausible geology. The multiscale parameterization of property fields reduces the tendency to underestimate uncertainty while still providing reservoir models that match data.

scholarly journals Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations

2021 ◽  
Vol 11 (5) ◽  
pp. 2165
Author(s):  
Sulaiman A. Alarifi
Keyword(s):  
Flow Rate ◽  
History Matching ◽  
Maximum Flow ◽  
Horizontal Wells ◽  
Maximum Flow Rate ◽  
Production Data ◽  
Shale Formations ◽  
Production History ◽  
Early Production ◽  
Fractured Horizontal Wells

A comprehensive overview and analysis of the productivity of 1216 recently abandoned multi-stage hydraulically fractured horizontal wells from five shale formations in the United States (US) is presented in this study. In this study, two decline curve analysis (DCA) methods were used to match actual production history data using least-squares fitting to find the best fit production parameters to reliably forecast production. The production history matching conducted resulted in very accurate matches (correlation coefficient of 0.99) between actual production data and the two DCA methods (Arps hyperbolic decline and stretched exponential production decline (SEPD) models). Using the outcomes from production history matching, universal averages of decline parameters for Arps hyperbolic decline and SEPD models were developed for each of the five formations. Furthermore, hindcasting was performed by matching a portion of the known production history and comparing the remaining portion of the known production history to the forecast. The Arps hyperbolic decline and SEPD methods were used to match production using only limited early production data (three months, six months, one year and two years). The main goals for fitting the DCA model to early production data was to estimate the optimum decline parameters that are then used to forecast production and estimate ultimate recovery. Production history matching using limited early production periods produced accurate production forecasts using as few as six months of production history (correlation coefficients between 0.85 and 0.94 using Arps hyperbolic decline). The main outcome of this study was a production analysis conducted on the production data of more than 1000 wells from five different shale formations to present the expected production behaviors of similar wells. Different production key performance indicators (KPIs) such as average well life, cumulative production volumes at different periods, average drop in production rate within the first year of production, average time to reach maximum flow rate, and the maximum flow rate were measured on all the wells from the five formations to provide an overview of the production performance of each formation.

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