Advanced methods for the evaluation of a hybrid-type unconventional play: The Bakken petroleum system

2016 ◽  
Vol 4 (2) ◽  
pp. SF93-SF111 ◽  
Author(s):  
Iain Pirie ◽  
Jack Horkowitz ◽  
Gary Simpson ◽  
John Hohman
Keyword(s):  
Evaluation Methods ◽  
Petroleum System ◽  
Oil Field ◽  
Probabilistic Methods ◽  
Rock Properties ◽  
Depth Interval ◽  
Free Fluid ◽  
Formation Evaluation ◽  
Hybrid Type ◽  
Petrophysical Evaluation

Hybrid-type plays such as the Bakken petroleum system (BPS) can be particularly challenging from an interpretation, completion, or production perspective due to the mix of conventional and unconventional elements coexisting within a relatively short depth interval. In the BPS, conventional aspects include the presence of separate reservoir intervals, which, depending on your location within the basin, may include the Scallion, Middle Bakken, Sanish, and Three Forks. Unconventional aspects include the Lower Bakken and Upper Bakken shales, which are organic-rich shales comprising source rock and reservoir. Developing an accurate petrophysical evaluation of these formations requires a priori knowledge of the mineralogy, fluids, and geomechanical properties such that appropriate logging measurements, core analysis methods, and interpretation techniques can be obtained and used. During the development phase of an oil field, the log and core measurements being acquired and the petrophysical evaluation being performed may vary significantly from well to well across the field. Some wells may have triple-combo wireline or logging-while-drilling measurements consisting of bulk density, neutron porosity, and induction or laterolog resistivity, supplemented with a total gamma ray measurement. Borehole sonic logs may also have been acquired in some wells primarily for seismic calibration, geomechanical modeling, and hydraulic stimulation design. If the “standard” suite of measurements and petrophysical evaluation being provided fail to accurately represent the true complexity of the formations being evaluated, the asset valuation will, in most cases, be negatively impacted. Our formation evaluation of the BPS led to the identification of unique petrophysical challenges for each of the formations comprising the BPS. Traditional formation evaluation methods were applied to the BPS based on triple-combo measurements, a traditional petrophysical analysis, and the evaluation of net feet of pay. Advanced evaluation methods and techniques were then applied to address the petrophysical complexities identified with core evaluation, advanced log measurements, and discrepancies between the two. New petrophysical models were developed and fine-tuned to address the shortcomings of the simple models, and the net feet of pay were reevaluated using these new models. The detailed formation evaluation program used to characterize the BPS consisted of standard triple-combo logs supplemented with advanced downhole measurements including: (1) triaxial resistivity for thin-bed analysis, (2) nuclear magnetic resonance for porosity, free-fluid, and kerogen identification, (3) dielectric dispersion for water saturation, (4) geochemical spectroscopy for mineralogy and total organic carbon, and (5) dipole sonic for dynamic rock properties. Petrophysical models were developed using deterministic and probabilistic methods to integrate the measurements acquired for the most accurate analysis of porosity, saturation, and mineralogy and to best describe the hydrocarbon production potential of the BPS.

FACIES CLASSIFICATION OF A COMPLEX RESERVOIR USING MACHINE LEARNING: CASE STUDY FROM VOLCANIC FORMATION, THE YURIHARA OIL FIELD, JAPAN

2021 ◽  
Author(s):  
Yuki Maehara ◽  
◽  
Takeaki Otani ◽  
Tetsuya Yamamoto ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Pillow Lava ◽  
Gaussian Mixture ◽  
Oil Field ◽  
Well Logs ◽  
Depth Interval ◽  
Volcanic System ◽  
Facies Classification ◽  
Volcanic Formation ◽  
Borehole Image

Lithological facies classification using well logs is essential in the reservoir characterization. The facies are manually classified from characteristic log responses derived, which is challenging and time consuming for geologically complex reservoirs due to high variation of log responses for each facies. To overcome such a challenge, machine learning (ML) is helpful to determine characteristic log responses. In this study, we classified the lithofacies by applying ML to the conventional well logs for the volcanic formation, onshore, northeast Japan. The volcanic formation of the Yurihara oil field is petrologically classified into five lithofacies: mudstone, hyaloclastite, pillow lava, sheet lava, and dolerite, with pillow lava being predominant reservoir. The former four lithofacies are the members of the volcanic system in Miocene, and dolerite randomly intruded later into those. Understanding the distribution of omnidirectional tight dykes at the well location is important for the estimation of potential near-lateral seal distribution compartmentalizing the reservoir. The facies are best classified by core data, which are unfortunately available in a limited number of wells. The conventional logs, with the help of the borehole image log, have been used for the facies classification in most of the wells. However, distinguishing dolerite from sheet lava by manual classification is very ambiguous, as they appear similar in these logs. Therefore, automated clustering of well logs with ML was attempted for the facies classification. All the available log data was audited in the target well prior to applying ML. A total of 10 well logs are available in the reservoir depth interval. To prioritize the logs for the clustering, the information of each log was first analyzed by Principal Component Analysis (PCA). The dimension of variable space was reduced from 10 to 5 using PCA. Final set of 5 variables, gamma-ray, density, formation photoelectric factor, neutron porosity, and laterolog resistivity, were used for the next clustering process. ML was applied to the selected 5 logs for automated clustering. Cross-Entropy Clustering (CEC) was first initialized using k-means++ algorithm. Multiple initialization processes were randomly conducted to find the global minimum of cost function, which automatically derived the optimized number of classes. The resulting classes were further refined by the Gaussian Mixture Model (GMM) and subsequently by the Hidden Markov Model (HMM), which takes the serial dependency of the classes between successive depths into account. Resulting 14 classes were manually merged into 5 classes referring to the lithofacies defined by the borehole image log analysis. The difference of the log responses between basaltic sheet lava and dolerite was too subtle to be captured with confidence by the conventional manual workflow, while the ML technique could successfully capture it. The result was verified by the petrological analyses on sidewall cores (SWCs) and cuttings. In this study, the automated clustering with the combination of several ML algorithms was demonstrated more efficient and reasonable facies classification. The unsupervised learning approach would provide supportive information to reveal the regional facies distribution when it is applied in the other wells, and to comprehend the dynamic behavior of the fluids in the reservoir.

Application of Computational Intelligence in Generating Synthetic Reservoir Rock Mechanical Parameters for Building Geo-Models.

2015 ◽  
Author(s):  
L. C. Akubue ◽  
A.. Dosunmu ◽  
F. T. Beka
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Numerical Models ◽  
Petroleum Industry ◽  
Oil Field ◽  
Wellbore Stability ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Earth Model ◽  
Sonic Log

Abstract Oil field Operations such as wellbore stability Management and variety of other activities in the upstream petroleum industry require geo-mechanical models for their analysis. Sometimes, the required subsurface measurements used to estimate rock parameters for building such models are unavailable. On this premise, past studies have offered variety of methods and investigative techniques such as empirical correlations, statistical analysis and numerical models to generate these data from available information. However, the complexity of the relationships that exists between the natural occurring variables make the aforementioned techniques limited. This work involves the application of Artificial Neural Networks (ANNs) to generating rock properties. A three-layer back-propagation neural network model was applied predicting pseudo-sonic data using conventional wireline log data as input. Four well data from a Niger-Delta field were used in this study, one for training, one for validating and the two others for generating and testing results. The network was trained with different sets of initial random weights and biases using various learning algorithms. Root mean square error (RMSE) and correlation coefficient (CC) were used as key performance indicators. This Neural-Network-Generated-Sonic-log was compared with those generated with existing correlations and statistical analysis. The results showed that the most influential input vectors with various configurations for predicting sonic log were Depth-Resistivity-Gamma ray-Density (with correlating coefficient between 0.7 and 0.9). The generated sonic was subsequently used to compute for other elastic properties needed to build mechanical earth model for evaluating the strength properties of drilled formations, hence optimise drilling performance. The models are useful in Minimizing well cost, as well as reducing Non Productive Time (NPT) caused by wellbore instability. This technique is particularly useful for mature fields, especially in situations where obtaining this well logs are usually not practicable.

Hydrocarbon charge in the onshore Canning Basin: insights from quantitative fluorescence and fluid inclusion investigations

2011 ◽  
Vol 51 (2) ◽  
pp. 706
Author(s):  
Keyu Liu ◽  
Ameed Ghori ◽  
Richard Kempton ◽  
Peter Eadington ◽  
Stephen Fenton ◽  
...  
Keyword(s):  
Fluid Inclusion ◽  
Petroleum System ◽  
Spectral Signature ◽  
Depth Interval ◽  
Widespread Occurrence ◽  
Canning Basin ◽  
Group A ◽  
Multiple Reservoir ◽  
Hydrocarbon Charge ◽  
Quantitative Fluorescence

The vast and mostly onshore Canning Basin—with an area of approximately 595,000 m2—is the least explored onshore sedimentary basin in Australia. As part of the petroleum system assessment carried out by WA DMP, more than 160 samples were investigated from eight wells in the onshore Canning Basin—they are: Acacia-1 Dodonea-1 Dodonea-2 Lake Hevern-1 Looma-1 White Hill-1 Wilson Cliffs-1 Yulleroo-1. Fluid inclusion and quantitative fluorescence techniques developed by CSIRO were used, including: The grains containing oil inclusions (GOITM) technique; The quantitative grain fluorescence (QGF) technique; QGF on extracts (QGF-E); and, the total scanning fluorescence (TSF) technique. The results reveal a widespread occurrence of hydrocarbon shows in the reservoir intervals investigated—7–8 wells showed evidence of oil migration and/or accumulations often occurring at multiple depth intervals. In White Hill-1, elevated QGF and QGF-E responses were recorded in the sandy units in a depth interval of more than 500 m in the Fairfield Group. A residual or palaeo oil column of >20 m gross height at 1,655 m was apparent from the QGF and QGF-E depth profiles—and GOI and TSF data. Oil inclusions from the Fairfield Group in White Hill-1 show spectral signature typical of thermally mature and light-medium API gravity. The TSF results also indicate the presence of some condensate species, as well as relatively heavy and possibly bio-degraded oils. The new fluid inclusion and fluorescence data provide direct evidence of an active petroleum system in the Canning Basin at multiple reservoir intervals, which may be of local significant quantity.

AWAKENING OF A SLEEPING GIANT: SUNRISE- TROUBADOUR GAS-CONDENSATE FIELD

2000 ◽  
Vol 40 (1) ◽  
pp. 417 ◽  
Author(s):  
R.J. Seggie ◽  
R.B. Ainsworth ◽  
D.A.Johnson ◽  
J.P.M. Koninx ◽  
B. Spaargaren ◽  
...  
Keyword(s):  
Gas Condensate ◽  
Probabilistic Methods ◽  
Rock Properties ◽  
Quality Data ◽  
Reservoir Properties ◽  
Water Contact ◽  
Fluid Analysis ◽  
Avo Inversion ◽  
Active Tectonism ◽  
Key Aspects

The Sunrise and Troubadour fields form a complex of giant gas-condensate accumulations located in the Timor Sea some 450 km northwest of Darwin. Left unappraised for almost a quarter of a century since discovery, recently renewed attention has brought these stranded hydrocarbon accumulations to the point of comm-ercialisation.A focussed appraisal program during 1997–1999 driven by expectations of growth in LNG and domestic gas markets, involved the acquisition and processing of an extensive grid of modern 2D seismic and the drilling, coring and testing of three wells. The aim of this program was to quantify better both in-place hydrocarbon volumes (reservoir properties and their distribution) and hydrocarbon recovery efficiency (gas quality and deliverability). Maximum value has been extracted from these data via a combination of deterministic and probabilistic methods, and the integration of analyses across all disciplines.This paper provides an overview of these efforts, describes the fields and details major subsurface uncertainties. Key aspects are:3D, object-based geological modelling of the reservoir, covering the spectrum of plausible sedimentological interpretations.Convolution of rock properties, derived from seismic (AVO) inversion, with 3D geological model realisations to define reservoir properties in inter-well areas.Incorporation of faults (both seismically mapped and probabilistically modelled sub-seismic faults) into both the static 3D reservoir models and the dynamic reservoir simulations.Interpretation of a tilted gas-water contact apparently arising from flow of water in the Plover aquifer away from active tectonism to the north.Extensive gas and condensate fluid analysis and modelling.Scenario-based approach to dynamic modelling.In summary, acquisition of an extensive suite of quality data during the past two-three years coupled with novel, integrated, state-of-the-art analysis of the subsurface has led to a major increase in estimates of potentially recoverable gas and condensate. Improved volumetric confidence in conjunction with both traditional and innovative engineering design (e.g. Floating Liquefied Natural Gas technology) has made viable a range of possible commercial developments from 2005 onwards.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

scholarly journals Analyzing of Drill Stem Test (DST) Result for Dual Porosity Limestone Reservoir

2017 ◽  
Vol 2 (3) ◽  
pp. 240-251
Author(s):  
Zheno Kareem Ahmed ◽  
Halkawt Ismail Ismail M-Amin
Keyword(s):  
Transient Flow ◽  
Double Porosity ◽  
Oil Field ◽  
Dual Porosity ◽  
Depth Interval ◽  
Reservoir Pressure ◽  
Well Test ◽  
Positive Skin ◽  
Skin Factor ◽  
Wellbore Storage

The aim of this paper is to discuss and evaluate the result of DST which was conducted in a limestone reservoir of an oil field at the depth interval 3764.29-3903.0 meter in well-1 to evaluate the dynamic characteristics of the reservoirs, for instance: skin effect, permeability, wellbore storage, reservoir boundary and average reservoir pressure. Reservoir Pressure profiles has been recorded for both Buildup and draw down intervals.  Semi-log and log-log coordinates have been used to plot the pressure signature date of both buildup period and its derivative to improve diagnostic and Horner plot. In addition, a dual porosity reservoir and infinite acting characteristic was discovered as a result of the well test data interpretation. Wellbore storage, skin factor and transient flow effects have been detected in the DST analysis on the dual porosity behavior due to phase re distribution.  Using final buildup sections, the flow parameters of dual porosity reservoir were determined as the flow between fissure and matrix was (7.558 x 10-6) while, the storability ratio between fissure and matrix was calculated as 0.3 and permeability is 102 MD for both matrix and the fissure together. However, negative value of skin factor mostly appears in double porosity limestone reservoirs, positive skin factor of the reservoir has been observed in this study. It can be considered that the positive skin factor can be resulted in either the formation was partially penetrated and /or wells were not cleaned up properly.

scholarly journals Mechanical Rock Properties Estimation for Carbonate Reservoir Using Laboratory Measurement: A Case Study from Jeribe, Khasib and Mishrif Formations in Fauqi Oil Field

2021 ◽  
Vol 54 (1E) ◽  
pp. 88-102
Author(s):  
Qahtan Abdul Aziz ◽  
Hassan Abdul Hussein
Keyword(s):  
Mechanical Properties ◽  
Bulk Density ◽  
Depositional Environments ◽  
Carbonate Reservoir ◽  
Oil Field ◽  
Well Logs ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Empirical Correlations ◽  
Rock Mechanical Properties

Estimation of mechanical and physical rock properties is an essential issue in applications related to reservoir geomechanics. Carbonate rocks have complex depositional environments and digenetic processes which alter the rock mechanical properties to varying degrees even at a small distance. This study has been conducted on seventeen core plug samples that have been taken from different formations of carbonate reservoirs in the Fauqi oil field (Jeribe, Khasib, and Mishrif formations). While the rock mechanical and petrophysical properties have been measured in the laboratory including the unconfined compressive strength, Young's modulus, bulk density, porosity, compressional and shear -waves, well logs have been used to do a comparison between the lab results and well logs measurements. The results of this study revealed that petrophysical properties are consistent indexes to determine the rock mechanical properties with high performance capacity. Different empirical correlations have been developed in this study to determine the rock mechanical properties using the multiple regression analysis. These correlations are UCS-porosity, UCS-bulk density, UCS-Vs, UCs-Vp Es-Vs, Es-Vp, and Vs-Vp. (*). For example, the UCS-Vs correlation gives a good determination coefficient (R2= 0.77) for limestone and (R2=0.94) for dolomite. A comparison of the developed correlations with literature was also checked. This study presents a set of empirical correlations that can be used to determine and calibrate the rock mechanical properties when core samples are missing or incomplete.

scholarly journals Analysis of the Petroleum System of Dima Oil Field by Using PetroMoD 1D

2021 ◽  
pp. 526-531
Author(s):  
Haider A. F. Al-Tarim
Keyword(s):  
Source Rock ◽  
Oil And Gas ◽  
Petroleum System ◽  
Oil Field ◽  
Reservoir Rock ◽  
Geological Time ◽  
Reservoir Rocks ◽  
Petroleum Systems ◽  
Geological Age ◽  
Stratigraphic Succession

The study of petroleum systems by using the PetroMoD 1D software is one of the most prominent ways to reduce risks in the exploration of oil and gas by ensuring the existence of hydrocarbons before drilling.      The petroleum system model was designed for Dima-1 well by inserting several parameters into the software, which included the stratigraphic succession of the formations penetrating the well, the depths of the upper parts of these formations, and the thickness of each formation. In addition, other related parameters were investigated, such as lithology, geological age, periods of sedimentation, periods of erosion or non-deposition, nature of units (source or reservoir rocks), total organic carbon (TOC), hydrogen index (HI) ratio of source rock units, temperature of both surface and formations as they are available, and well-bottom temperature.      Through analyzing the models by the evaluation of the source rock units, the petrophysical properties of reservoir rock units, and thermal gradation with the depth during the geological time, it became possible to clarify the elements and processes of the petroleum system of the field of Dima. It could be stated that Nahr Umr, Zubair, and Sulaiy formations represent the petroleum system elements of Dima-1 well.

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