Determination of Geostatistical Parameters Using Well Test Data

In the present work, the resistance to crack extension of 2524-T3 aluminum alloy under Mode I loading was studied by using the middle-cracked tension M (T) specimens. The curve, plane-stress fracture toughness and apparent plane-stress fracture toughness were calculated by test data. The average value of measured fracture toughness at room temperature was 161 MPam1/2. The results and conclusions can be referred in airplane skin design.

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The Reliability of the Predicted Performance of Natural Gas Reservoirs Using Reservoir Parameters From Well Test Data Containing Errors

10.2118/5343-ms ◽

1975 ◽

Cited By ~ 2

Author(s):

Ali H. Dogru ◽

Roy M. Knapp

Keyword(s):

Natural Gas ◽

Test Data ◽

Well Test ◽

Gas Reservoirs ◽

Natural Gas Reservoirs

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Determination of Spot Weld Modeling Parameters from Test Data for Finite Element Crash Simulation

10.4271/2004-01-0692 ◽

2004 ◽

Cited By ~ 2

Author(s):

Chelliah Madasamy ◽

Tau Tyan ◽

Omar Faruque ◽

Thierry Guimberteau

Keyword(s):

Finite Element ◽

Test Data ◽

Spot Weld ◽

Crash Simulation

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Connecting Data, Model and Process to Streamline and Enhance Single-Point Production Well Test Validation

10.2118/207270-ms ◽

2021 ◽

Author(s):

Mohamad Mustaqim Mokhlis ◽

Nurdini Alya Hazali ◽

Muhammad Firdaus Hassan ◽

Mohd Hafiz Hashim ◽

Afzan Nizam Jamaludin ◽

...

Keyword(s):

Test Data ◽

Single Point ◽

Database Systems ◽

Test Validation ◽

Well Test ◽

Production Well ◽

Well Test Analysis ◽

Test Analysis ◽

Digital Ecosystem ◽

Well Model

Abstract In this paper we will present a process streamlined for well-test validation that involves data integration between different database systems, incorporated with well models, and how the process can leverage real-time data to present a full scope of well-test analysis to enhance the capability for assessing well-test performance. The workflow process demonstrates an intuitive and effective way for analyzing and validating a production well test via an interactive digital visualization. This approach has elevated the quality and integrity of the well-test data, as well as improved the process cycle efficiency that complements the field surveillance engineers to keep track of well-test compliance guidelines through efficient well-test tracking in the digital interface. The workflow process involves five primary steps, which all are conducted via a digital platform: Well Test Compliance: Planning and executing the well test Data management and integration Well Test Analysis and Validation: Verification of the well test through historical trending, stability period checks, and well model analysis Model validation: Correcting the well test and calibrating the well model before finalizing the validity of the well test Well Test Re-testing: Submitting the rejected well test for retesting and final step Integrating with corporate database system for production allocation This business process brings improvement to the quality of the well test, which subsequently lifts the petroleum engineers’ confidence level to analyze well performance and deliver accurate well-production forecasting. A well-test validation workflow in a digital ecosystem helps to streamline the flow of data and system integration, as well as the way engineers assess and validate well-test data, which results in minimizing errors and increases overall work efficiency.

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

10.2118/206137-ms ◽

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.

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