Abstract

In response to declining oil production in Kazakhstan’s Uzen oilfield (over 9,000 wells), a renovation program was launched. The program’s objective is to review the geological model, FDP, and address petrophysical challenges associated with a significant volume of data, time constraints, resource limitations, and poor log data quality. The conventional approaches proved inadequate to handle these challenges efficiently. To overcome these obstacles, an AI/ML-based solution was employed to perform logs quality check, data reconstruction, and address petrophysical complexities. Our study utilized an Automated Log Editing solution for the entire process of petrophysical data processing. We started with log data preparation stage which include harmonization of mnemonics and units of measurements. Subsequently, we employed a machine learning (ML) approach, encompassing further exploratory data analysis to remove non-physical data points, followed by outlier detection to identify anomalous intervals. To establish well-level proximity, we utilized a similarity analysis algorithm based on petrophysical properties, and the resulting relationships were leveraged for log reconstruction. The analysis of the project results revealed that approximately 15% of the wells required significant log corrections. These corrections primarily focused on washed-out intervals, where inaccuracies were observed in the logs. Additionally, intervals with incorrect neutron porosity calculations were identified and promptly corrected. The reliability and accuracy of the project results were validated by comparing them with the high-quality NMR logs from the three wells. The NMR logs served as a valuable reference  for formation evaluation, ensuring the integrity of the project outcomes. The utilization of the automated log editing solution proved instrumental in reducing data processing time and minimizing bias during log reconstruction. By swiftly identifying and loading missing data, the project enhanced the efficiency of the data auditing process. The automated approach demonstrated its effectiveness in identifying and correcting log discrepancies. The successful implementation of the project highlights the importance of utilizing automated log editing solutions for accurate data auditing and log corrections in well logging. By incorporating high-quality NMR logs as a validation tool, the project outcomes were verified and ensured to meet the required standards of reliability. This approach contributes to improving the overall quality and reliability of log data in formation evaluation. This paper will present novel information by demonstrating the successful utilization of historically available Soviet Union data, including logs like SP, acquired over a 60-year. The use of custom-built and adapted algorithms in the project sets it apart from previous research in the field. The findings of this study will provide valuable insights to professionals in the industry, offering a new approach to leveraging old data, reducing the requirement for additional wellsite operations, and minimizing associated CO2 emissions.  

Introduction 

 The Uzen oil and gas field is located in the Mangystau region of the Republic of Kazakhstan, on the Mangyshlak Peninsula. It is a multi-layered field characterized by a complex structure, where its oil and gas reserves are predominantly found within the terrigenous section of Jurassic-Cretaceous deposits. The discovery of the Uzen field dates back to 1961, and it has been under the operation of JSC Ozenmunaigas since 1996. Wireline logging was conducted in the field in wells primarily drilled with a 215.9 mm diameter bit. Reservoir sections in exploration and production wells were drilled using water-based mud ranging in density from 1.18 to 1.28 g/cc. In recent times, the pressing need to develop a distinct exploration plan for the region has emerged due to the depletion of the oil field and the anticipated decline in oil production. The field rehabilitation plan encompassed additional 3D seismic surveys and a comprehensive reinterpretation of the entire wells. Given the use of diverse logging tools of varying types and qualities from the 1960s to the present, the incorporation of the available data into the 3D seismic volume for analyzing reservoir properties and rock types across the field has posed challenges. Standardizing the log data is imperative to reduce the variability stemming from factors such as the borehole environment, discrepancies in tool responses among different service providers, and modifications in tool design over time. Logging measurements were originally recorded in analog form until the 1990s, after which a transition was made to digital recording. Digitizing the older logs necessitates additional time and expertise to verify and comprehend scaling and calibration, log responses, and various other aspects, ensuring accurate values are derived from curves and rectified when necessary for use in various calculations.In various academic disciplines, the quality control (QC) of input data holds paramount importance, often constituting a crucial and time-intensive procedure. The initial phase of elastic log conditioning in petrophysics and rock physics studies, a tedious manual process, is pivotal. However, the advent of the machine learning technology has revolutionized this task, automating the  laborious aspects and liberating valuable expert  time.