An optimized XGBoost method for predicting reservoir porosity using petrophysical logs

2021 ◽  
pp. 109520
Author(s):  
Shaowei Pan ◽  
Zechen Zheng ◽  
Zhi Guo ◽  
Haining Luo
Keyword(s):  
Reservoir Porosity ◽  
Petrophysical Logs

scholarly journals Porosity evolution of high-quality reservoirs in deep Palaeogene lacustrine carbonate rocks in the central Bohai Sea

2018 ◽  
Vol 36 (5) ◽  
pp. 1136-1156 ◽  
Author(s):  
Yuanhua Qing ◽  
Zhengxiang Lü ◽  
Xiandong Wang ◽  
Xiuzhang Song ◽  
Shunli Zhang ◽  
...  
Keyword(s):  
Bohai Sea ◽  
Oil And Gas ◽  
Early Stage ◽  
Reservoir Rock ◽  
Secondary Porosity ◽  
Authigenic Minerals ◽  
Porosity Evolution ◽  
Lacustrine Carbonate ◽  
Reservoir Porosity ◽  
Pore Lining

The oil and gas in the Palaeogene lacustrine carbonate rock reservoirs in the Bohai Sea accumulated during several periods. The reservoir porosity formed during each period affected the degree of accumulation that occurred. In this paper, the percentages of particles, authigenic minerals and pores in the reservoir bed were calculated with the statistical method of microstructure analysis. The formation time was determined with an isotopic analysis of the authigenic carbonate minerals and the homogenization temperature of the gas–liquid inclusions. The percentages of the primary intergranular pores that formed during the different stages were recovered based on the compaction features both before and after the formation of the major authigenic minerals. The evolution of porosity was thus described quantitatively and chronologically, employing the percentages of the residual primary intergranular pores, visceral cavity pores and dissolved pores at the different burial depths. The results indicate that in the initial sediments of the reservoir rock, the primary intergranular porosity was 32.4%. During the early burial stage, the total reservoir porosity increased by up to 46.9%, due to the addition of another type of primary pore, namely visceral cavity pores, which were generated from the decomposition of bioclasts. During the late, deep burial stage, the compaction reduced only 8.2% of the porosity, due to the support of the pore-lining dolomite precipitating during the early stage. Authigenic minerals occupied 12.6% of the porosity, and the dissolution created the secondary porosity by 3.8%. Good preservation of the visceral cavity pores and the growth of the pore-lining dolomites during the early stages are the major factors leading to the high reservoir porosity. The quantitative and chronological characteristics of the reservoir porosity evolution could be described accurately. The prediction of reservoir beds can be better guided than in previously reported methods by applying high resolution microscopic quantitative analysis technology and authigenic mineral timing analysis technology.

scholarly journals THE ANALYSIS OF METHODS OF RESERVOIR POROSITY-PERMEABILITY PROPERTIES STUDY IN HORIZONTAL WELLS TO ESTIMATE THE BOTTOM-HOLE TREATMENT RESULTS

2016 ◽  
pp. 72-77
Author(s):  
I. I. Mannanov ◽  
L. I. Garipova
Keyword(s):  
Horizontal Wells ◽  
Practical Application ◽  
Treatment Results ◽  
Reservoir Properties ◽  
Bottom Hole ◽  
Reservoir Porosity ◽  
Analysis Of Dynamics

Obtaining of the information about reservoir properties of formations is the basis of designing and the results analysis of production stimulation methods. Now two directions have been widely studied and applied, i.e. the hydrodynamic studies implementation using the method of the level recovery and the prolong analysis of dynamics of well producing characteristics. The paper discusses the practical application of both approaches for estimation of the need in treatment and the results of production intensification methods.

AUTO-NAVIGATION ON PRESSURE AND SAMPLING LOCATION IN WIRELINE AND LWD: BIG DATA CHALLENGES AND SOLUTIONS

2021 ◽  
Author(s):  
Mehdi Alipour K ◽  
◽  
Bin Dai ◽  
Jimmy Price ◽  
Christopher Michaell Jones ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Expert Knowledge ◽  
Sampling Location ◽  
Neutron Density ◽  
Reserve Estimation ◽  
Pressure Test ◽  
Single Well ◽  
Application Deployment ◽  
Exploratory Data ◽  
Petrophysical Logs

Measuring formation pressure and collecting representative samples are the essential tasks of formation testing operations. Where, when and how to measure pressure or collect samples are critical questions which must be addressed in order to complete any job successfully. Formation testing data has a crucial role in reserve estimation especially at the stage of field exploration and appraisal, but can be time consuming and expensive. Optimum location has a major impact on both the time spent performing and the success of pressure testing and sampling. Success and optimization of rig-time paradoxically requires careful and extensive but also quick pre-job planning. The current practice of finding optimum locations for testing heavily rely on expert knowledge. With nearly complete digitization of data collection, the oil industry is now dealing with massive data flow giving rise to the question of its application and the necessity to collect. Some data may be so called “dark data” of which a very tiny portion is used for decision making. For instance, a variety of petrophysical logs may be collected in a single well to provide measures of formation properties. The logs may include conventional gamma ray, neutron, density, caliper, resistivity or more advanced tools such as high-resolution image logs, acoustic, or NMR. These data can be integrated to help decide where to pressure test and sample, however, this effort is nearly exclusively driven by experts and is manpower intensive. In this paper we present a workflow to gather, process and analyze conventional log data in order to optimize formation testing operations. The data is from an enormous geographic distribution of wells. Tremendous effort has been performed to extract, transform and load (ETL) the data into a usable format. Stored files contains multi-million to multi-billions rows of data thereby creating technology challenges in terms of reading, processing and analyzing in a timely manner for pre-job planning. We address the technological challenges by deploying cutting-edge data technology to solve this problem. Upon completion of the workflow we have been able to build a scalable petrophysical interpretation log platform which can be easily utilized for machine learning and application deployment. This type of data base is invaluable asset especially in places where there is a need for knowledge of analogous wells. Exploratory data analysis on worldwide data on mobility and some key influencing features on pressure test and sampling quality, is performed and presented. We further show how this data is integrated and analyzed in order to automate selection of locations for which to formation test.

RESERVOIR SCALE CHEMOSTRATIGRAPHY AND FACIES MODELING USING HIGH SAMPLE RATE GEOPHYSICAL SCANS OF WHOLE CORE

2021 ◽  
Author(s):  
John J. Degenhardt ◽  
◽  
Safdar Ali ◽  
Mansoor Ali ◽  
Brian Chin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
High Resolution ◽  
Operating Characteristics ◽  
Eagle Ford Shale ◽  
Eagle Ford ◽  
Vertical Heterogeneity ◽  
Austin Chalk ◽  
Sample Rate ◽  
Petrophysical Logs

Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.

Information Management And Mapping System For Subsurface Stratigraphic Analysis

1994 ◽  
Author(s):  
D. Gill ◽  
M. Levinger
Keyword(s):  
Operating System ◽  
Structural Analysis ◽  
Information Management ◽  
Computer Programs ◽  
Contour Maps ◽  
Subsurface Geology ◽  
Stratigraphic Analysis ◽  
Comprehensive Database ◽  
Mapping System ◽  
Petrophysical Logs

An information management and mapping system combining a series of interactive computer programs for stratigraphic, lithofacies, paleogeographic, and structural analysis interfaced with a comprehensive database on subsurface geology produces contour maps of quantitative variables including structure maps, isopach maps, and maps of lithofacies parameters; detailed lithologic and stratigraphic logs; and printouts of lithofacies parameters for all levels of the lithostratigraphic subdivision. Users communicate by means of simple, on-screen, menu-driven dialogues controlled by FORTRAN programs. The system runs on DEC/Micro VAX II computers operating under VMS. This information management and mapping system for subsurface stratigraphic analysis is an integration of a comprehensive database on the subsurface geology of Israel and a series of computer programs for stratigraphic, lithofacies, paleogeographic, and structural analysis. Development of the system, referred to as "ATLAS -RELIANT," was sponsored by OEIL [Israel Oil Exploration (Investment) Ltd.). The system serves primarily as a storage and retrieval facility for information on the subsurface geology of Israel. Users can obtain printouts of lithologic and stratigraphic logs, contour maps, and value maps. The system originally was developed to run on a CDC machine under the NOS/BE operating system. Later OEIL expanded the database to include many additional items of information [inventory of cores and petrophysical logs, results of production tests, results of petrophysical analyses, geochemical analyses of recovered fluids (water samples and hydrocarbons), and results of quantitative analyses of petrophysical logs] and the system was modified to run on DEC/Micro VAX II computers under the VMS operating system (Shertok, 1969). Among other things, the ATLAS-RELIANT system was instrumental in the regional stratigraphic analysis of the subsurface geology of Israel performed by OEIL during 1968-1988 (OEIL, 1966; Cohen et al., 1990). The database, dubbed "ATLAS," is about 16 MB in size and contains information on 320 petroleum exploration and development boreholes, 50 deep water wells, and 100 columnar sections of outcrops.

Petrophysical Rocks: Electrofacies and Lithofacies

2014 ◽  
Author(s):  
John H. Doveton
Keyword(s):  
Particle Size ◽  
Physical Properties ◽  
Sedimentary Rocks ◽  
Well Control ◽  
The Core ◽  
Lateral Extent ◽  
Petrophysical Logs ◽  
Definition Of

Many years ago, the classification of sedimentary rocks was largely descriptive and relied primarily on petrographic methods for composition and granulometry for particle size. The compositional aspect broadly matches the goals of the previous chapter in estimating mineral content from petrophysical logs. With the development of sedimentology, sedimentary rocks were now considered in terms of the depositional environment in which they originated. Uniformitarianism, the doctrine that the present is the key to the past, linked the formation of sediments in the modern day to their ancient lithified equivalents. Classification was now structured in terms of genesis and formalized in the concept of “facies.” A widely quoted definition of facies was given by Reading (1978) who stated, “A facies should ideally be a distinctive rock that forms under certain conditions of sedimentation reflecting a particular process or environment.” This concept identifies facies as process products which, when lithified in the subsurface, form genetic units that can be correlated with well control to establish the geological architecture of a field. The matching of facies with modern depositional analogs means that dimensional measures, such as shape and lateral extent, can be used to condition reasonable geomodels, particularly when well control is sparse or nonuniform. Most wells are logged rather than cored, so that the identification of facies in cores usually provides only a modicum of information to characterize the architecture of an entire field. Consequently, many studies have been made to predict lithofacies from log measurements in order to augment core observations in the development of a satisfactory geomodel that describes the structure of genetic layers across a field. The term “electrofacies” was introduced by Serra and Abbott (1980) as a way to characterize collective associations of log responses that are linked with geological attributes. They defined electrofacies to be “the set of log responses which characterizes a bed and permits it to be distinguished from the others.” Electrofacies are clearly determined by geology, because physical properties of rocks. The intent of electrofacies identification is generally to match them with lithofacies identified in the core or an outcrop.

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