Understanding subsurface fluvial architecture from a combination of geological well test models and well test data

2018 ◽  
Vol 488 (1) ◽  
pp. 237-257 ◽  
Author(s):  
Patrick William Michael Corbett ◽  
Gleyden Lucila Benítez Duarte
Keyword(s):  
Test Data ◽  
History Matching ◽  
Reservoir Characterization ◽  
Real Life ◽  
Well Testing ◽  
Well Test ◽  
Test Models ◽  
Geological Models ◽  
Single Well ◽  
Porosity And Permeability

AbstractTwo decades of geological modelling have resulted in the ability to study single-well geological models at a sufficiently high resolution to generate synthetic well test responses from numerical simulations in realistic geological models covering a range of fluvial styles. These 3D subsurface models are useful in aiding our understanding and mapping of the geological variation (as quantified by porosity and permeability contrasts) in the near-wellbore region. The building and analysis of these models enables many workflow steps, from matching well test data to improving history-matching. Well testing also has a key potential role in reservoir characterization for an improved understanding of the near-wellbore subsurface architecture in fluvial systems. Developing an understanding of well test responses from simple through increasingly more complex geological scenarios leads to a realistic, real-life challenge: a well test in a small fluvial reservoir. The geological well testing approach explained here, through a recent fluvial case study in South America, is considered to be useful in improving our understanding of reservoir performance. This approach should lead to more geologically and petrophysically consistent models, and to geologically assisted models that are both more correct and quicker to match to history, and thus, ultimately, to more useful reservoir models. It also allows the testing of a more complex geological model through the well test response.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

scholarly journals Data Processing Method of Well Testing

2021 ◽  
Vol 134 (3) ◽  
pp. 35-38
Author(s):  
A. M. Svalov ◽  
Keyword(s):  
Boundary Condition ◽  
Asymptotic Expansion ◽  
Test Data ◽  
Traditional Method ◽  
Processing Method ◽  
Well Testing ◽  
Well Test ◽  
Conductivity Equation ◽  
Complete Asymptotic Expansion ◽  
Data Processing Method

Horner’s traditional method of processing well test data can be improved by a special transformation of the pressure curves, which reduces the time the converted curves reach the asymptotic regimes necessary for processing these data. In this case, to take into account the action of the «skin factor» and the effect of the wellbore, it is necessary to use a more complete asymptotic expansion of the exact solution of the conductivity equation at large values of time. At the same time, this method does not allow to completely eliminate the influence of the wellbore, since the used asymptotic expansion of the solution for small values of time is limited by the existence of a singular point, in the vicinity of which the asymptotic expansion ceases to be valid. To solve this problem, a new method of processing well test data is proposed, which allows completely eliminating the influence of the wellbore. The method is based on the introduction of a modified inflow function to the well, which includes a component of the boundary condition corresponding to the influence of the wellbore.

scholarly journals Pressure and pressure derivative analysis for non-newtonian pseudoplastic fluids in double-porosity formations

2011 ◽  
Vol 4 (3) ◽  
pp. 47-60 ◽  
Author(s):  
Freddy-Humberto Escobar ◽  
Angela-Patricia Zambrano ◽  
Diana-Vanessa Giraldo ◽  
José-Humberto Cantillo
Keyword(s):  
Oil Recovery ◽  
Reservoir Characterization ◽  
Numerical Models ◽  
Simulated Data ◽  
Newtonian Fluids ◽  
Well Testing ◽  
Well Test ◽  
Well Test Analysis ◽  
Pressure Derivative ◽  
Derivative Analysis

Non-Newtonian fluids are often used during various drilling, workover and enhanced oil recovery processes. Most of the fracturing fluids injected into reservoir-bearing formations possess non-Newtonian nature and these fluids are often approximated by Newtonian fluid flow models. In the field of well testing, several analytical and numerical models taking into account Bingham, pseudoplastic and dilatant non-Newtonian behavior have been introduced in the literature to study their transient nature in porous media for a better reservoir characterization. Most of them deal with fracture wells and homogeneous formations and well test interpretation is conducted via the straight-line conventional analysis or type-curve matching. Only a few studies consider the pressure derivative analysis. However, there exists a need of a more practical and accurate way of characterizing such systems. So far, it does not exist any methodology to characterize heterogeneous formation bearing non-Newtonian fluids through of well test analysis.  In this study, an interpretation methodology using the pressure and pressure derivative log-log plot is presented for non-Newtonian fluids in naturally fractured formations, so the dimensionless fracture storativity ratio, ω, and interporosity flow parameter, λ, are obtained from characteristics points found on such plot. The developed equations and correlations are successfully verified by their application only to synthetic well test data since no actual field data are available. A good match is found between the results provided by the proposed technique and the values used to generate the simulated data.  

Bringing Flexibility and Automation to Well Testing Operations Through Wireless Telemetry - Case Study

2021 ◽  
Author(s):  
Elias Temer ◽  
Deiveindran Subramaniam ◽  
Yermek Kaipov ◽  
Carlos Merino ◽  
Vladimirovich Latvin ◽  
...  
Keyword(s):  
Real Time ◽  
Reservoir Characterization ◽  
Time Window ◽  
Weather Conditions ◽  
Development Planning ◽  
Testing Time ◽  
Well Testing ◽  
Well Test ◽  
Real Time Control ◽  
Field Development

Abstract Dynamic reservoir data are a key driver for operators to meet the forecasted production investments of their fields. However, many challenges during well testing, such as reduced exploration and capex budgets, complex geologic structures, and inclement weather conditions that reduce the well testing time window can prevent them from gathering critical reservoir characterization data needed to make more informed field development planning decisions. To overcome these challenges, a live, downhole reservoir testing platform enabled the most representative reservoir information in real time and connected more zones of interest in a single run for appraisal wells in the Sea of Okhotsk, Russia. This paper describes the test requirements, the prejob planning, and automated execution of wirelessly enabled operations that led to the successful completion of the well test campaign in very hostile conditions, a remote area, and restricted period. The use of a telemetry system to well testing in seven zones enabled real-time control of critical downhole equipment and acquired data at surface, which in turn was transmitted to the operator's office in town in real time. Various operation examples will be discussed to demonstrate how automated data acquisition and downhole operations control has been used to optimize operations by both the service company and the operator.

Assimilating Microseismic and Well-Test Data by Use of EnKF for Accurate Reservoir Characterization

SPE Journal ◽  
2014 ◽  
Vol 20 (01) ◽  
pp. 186-201 ◽  
Author(s):  
Mei Han ◽  
Gaoming Li ◽  
Jingyi Chen
Keyword(s):  
Test Data ◽  
Reservoir Characterization ◽  
Weighted Average ◽  
Well Test ◽  
Sampling Errors ◽  
Reservoir Properties ◽  
Reservoir Prediction ◽  
Pressure Transient ◽  
Microseismic Data ◽  
Transient Data

Summary The pressure-transient well-test data can be used to determine the thickness-weighted average permeability in a multilayer reservoir. Injection- or production-profile logs (layer rates), if available, may be used to further quantify the layer properties. This paper explores the possibility of the use of microseismic data in place of injection-/production-profile logs for layered-reservoir characterization. The microseismic first-arrival times from the perforation-timing shots of the test well to monitor wells can only resolve the average velocity along its wavepath but are more sensitive to the layer (or region) with high wave velocity (low productivity). On the contrary, the pressure-transient data are more sensitive to the properties of the high-productivity (high-permeability) layers. Therefore, these two types of data are complementary in reservoir characterization. In this paper, we assimilate these two types of data by use of the state-of-the-art ensemble-Kalman-filter (EnKF) method. Layered-homogeneous- and layered-heterogeneous-reservoir examples verified the complementary nature of these two types of data. The porosities and permeabilities in the layered reservoir obtained after assimilating both types of data are comparable with assimilating pressure-transient and layer-rate data. EnKF is a stochastic process, and the final results may depend on the initial ensemble because of sampling errors, sample size, and nonlinearity of the problem. In this paper, we generated 10 different ensembles for each example for better uncertainty quantification. The paper shows that assimilating pressure-transient data only will yield biased estimates of layered-reservoir properties, whereas assimilating both pressure and microseismic data improves the reservoir-property estimation and reservoir-prediction capabilities.

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.

Calibrate Flow Simulation Models with Well-Test Data to Improve History Matching

1999 ◽  
Author(s):  
Nanqun He ◽  
K.T. Chambers
Keyword(s):  
Test Data ◽  
History Matching ◽  
Flow Simulation ◽  
Simulation Models ◽  
Well Test

scholarly journals Performing Pumping Test Data Analysis Applying Cooper-Jacob’s Method for Estimating of the Aquifer Parameters

2016 ◽  
Vol 12 (2) ◽  
pp. 9-20 ◽  
Author(s):  
Khider Mawlood Dana ◽  
Sabah Mustafa Jwan
Keyword(s):  
Test Data ◽  
Specific Capacity ◽  
Pumping Test ◽  
The Other ◽  
Well Test ◽  
Production Well ◽  
Observation Well ◽  
Aquifer Parameters ◽  
Aquifer Test ◽  
Single Well

Abstract Single well test is more common than aquifer test with having observation well, since the advantage of single well test is that the pumping test can be conducted on the production well with the absence of observation well. A kind of single well test, which is step-drawdown test used to determine the efficiency and specific capacity of the well, however in case of single well test it is possible to estimate Transmissivity, but the other parameter which is Storativity is overestimated, so the aim of this study is to analyze four pumping test data located in KAWRGOSK area by using cooper-Jacob’s (1946) time drawdown approximation of Theis method to estimate the aquifer parameters, also in order to determine the reasons which are affecting the reliability of the Storativity value and obtain the important aspect behind that in practice.

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