Pore-Facies as a tool for incorporation of small scale dynamic information in integrated reservoir studies

In this study, the quantification and incorporation of pore geometry, a qualitative parameter, and a source of dynamic information, will be demonstrated in the integrated reservoir studies. To quantify pore geometry, mercury injection capillary pressure (MICP) curves have been exploited. For each MICP curve, 20 parameters were derived and multi-resolution graph-based clustering was applied to classify the curves into nine representative distinct clusters. The number of clusters was determined based on petrography and cluster analysis. The quantified pore geometry in terms of discrete variable has been called pore-facies, and like electro-facies and litho-facies could be used in facies modelling and rock typing phases of an integrated study. The dependence of dynamic reservoir rock properties on pore geometry makes the pore-facies an interesting and powerful approach for incorporation of small-scale dynamic data into a reservoir model. A comparison among various facies definitions proved that neither litho-facies nor electro-facies is capable of characterizing dynamic rock properties, and the best results were achieved by the pore-facies method. Based on this study, it is recommended that for facies analysis in reservoir modelling, methods based on pore characteristics such as pore-facies, introduced in this paper, be used rather than traditional facies that rely on matrix properties. The next generation of the reservoir models which incorporate pore-facies-based rock types will provide a basis for more accurate static and dynamic models, a narrower range of uncertainty in the models, and a better prediction of reservoir performance

Permeability Prediction and Diagenesis in Tight Carbonates Using Machine Learning Techniques

Machine learning techniques have found their way into many problems in geoscience but have not been used significantly in the analysis of tight rocks. We present a case study testing the effectiveness of artificial neural networks and genetic algorithms for the prediction of permeability in tight carbonate rocks. The dataset consists of 130 core plugs from the Portland Formation in southern England, all of which have measurements of Klinkenberg-corrected permeability, helium porosity, characteristic pore throat diameter, and formation resistivity. Permeability has been predicted using genetic algorithms and artificial neural networks, as well as seven conventional ‘benchmark’ models with which the machine learning techniques have been compared. The genetic algorithm technique has provided a new empirical equation that fits the measured permeability better than any of the seven conventional benchmark models. However, the artificial neural network technique provided the best overall prediction method, quantified by the lowest root-mean-square error (RMSE) and highest coefficient of determination value (R2). The lowest RMSE from the conventional permeability equations was from the RGPZ equation, which predicted the test dataset with an RMSE of 0.458, while the highest RMSE came from the Berg equation, with an RMSE of 2.368. By comparison, the RMSE for the genetic algorithm and artificial neural network methods were 0.433 and 0.38, respectively. We attribute the better performance of machine learning techniques over conventional approaches to their enhanced capability to model the connectivity of pore microstructures caused by codependent and competing diagenetic processes. We also provide a qualitative model for the poroperm characteristics of tight carbonate rocks modified by each of eight diagenetic processes. We conclude that, for tight carbonate reservoirs, both machine learning techniques predict permeability more reliably and more accurately than conventional models and may be capable of distinguishing quantitatively between pore microstructures caused by different diagenetic processes

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done

Fuzzy rock typing: Enhancing reservoir simulation and modeling by honoring high resolution geological models

Rock typing is an essential part of building geologic model for an asset. Millions of dollars are invested in logs, core measurements, SCAL studies, and geological interpretation that result in definition of different rock types. Rock types overlap and do not have crisp boundaries.;Upon definition of rock types for a series of geological formations geoscientists use approximation of multiple and overlapping rock types to identify a dominant rock type for any grid block in a reservoir simulation model. This defeats the original purpose of performing detail geological, petrophysical studies as far as reservoir flow models are concerned.;The objective of this study is to develop a new and novel methodology based on performing fuzzy rock typing . Fuzzy rock typing refers to application of fuzzy set theory to the part of reservoir characterization that is concerned with rock type determination. Fuzzy set theory is applied in order to take into account the inherent uncertainties and vagueness associated with rock typing in hydrocarbon bearing reservoirs.;In this work, a numerical simulator has been used as the control environment in order to set up multiple studies that would demonstrate the differences between using conventional (current practices) approach of implementation of geologic models in the reservoir flow simulation studies and the new approach that is the subject of this study. By using the numerical reservoir simulator as the control environment it is intended to study the complexities that exist in upscaling the high resolution geological model using two different approaches.;The high resolution geological model used in its entirety and the flow simulation is performed. The results (production profiles) are compared to first, the upscaled model using conventional (current) practices and then the upscaled model using the proposed technique. The results are analyzed in order to demonstrate the difference between the two techniques and the advantages and disadvantages of each have been identified

Machine Learning Based Real-Time Quantification of Production from Individual Clusters in Shale Wells

Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore. Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine Learning. The technique presented provides continuous production log on demand thereby providing opportunities for the optimization of completions design and hydraulic fracture treatments of future planned wells. A Fiber-Optic sensing enabled horizontal well MIP-3H in the Marcellus Shale has been selected for this work. MIP-3H is a 28-stage horizontal well drilled in July 2015, as part of a Department of Energy (DOE)-sponsored project - Marcellus Shale Energy & Environment Laboratory (MSEEL). A one-day conventional production logging operation has been performed on MIP-3H using a flow scanner while the installed Fiber-Optic DTS unit has collected temperature measurements every three hours along the well since completion. An ensemble of machine learning models has been developed using as input the DTS measurements taken during the production logging operation, details of mechanical logs, completions design and hydraulic fracture treatments data of the well to develop the real-time shale gas production monitoring tool

Neural network applications to reservoirs: Physics-based models and data models

International audienceEditoria

Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia

Shear wave velocity associated with compressional wave velocity can provide the accurate data for geophysical study of a reservoir. These so called petroacoustic studies have important role in reservoir characterization such as lithology determination, identifying pore fluid type, and geophysical interpretation. In this study, a fuzzy logic, a neuro-fuzzy and an artificial neural network approaches were used as intelligent tools to predict shear wave velocity from petrophysical data. The petrophysical data of two wells were used for constructing intelligent models in a sandstone reservoir of Carnarvon Basin, NW Shelf of Australia. A third well of the field was used to evaluate the reliability of the models. The results show that intelligent models have been successful for prediction of shear wave velocity from conventional well log data

Supervised intelligent committee machine method for hydraulic conductivity estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers

Study of hydraulic fracturing for gas drainage in a coalmine in Iran

This is the author accepted manuscript. The final version is available from Thomas Telford (ICE Publishing) via the DOI in this record.Hydraulic fracturing (HF) is one of the methods to make coalmining operation safer and more economic. One of the hazards in underground coal mining operation is the sudden coal gas emission leading to coal explosion. To reduce the risk of gas emissions to ensure safer mining, it is necessary to pre-drain coal seams and surrounding layers. The most important parameters affecting the HF process of a coal seam are: dip, thickness, seam uniformity, roof and floor conditions, reserve of coal seam and coal strength. This paper presents the development and application of a fuzzy model to predict the efficiency of hydraulic fracturing, considering the above factors. In the developed model, the efficiency of hydraulic fracturing of coal seams is calculated as a dimensionless numerical index within the range 0-100. The suggested numerical scale categorizes the efficiency of HF of seams to very low, low, medium, high and very high, each one being specified by a numerical range as a subset of the above range (0-100). The model is used to study the potential of hydraulic fracturing in a coal bed in PARVADEH 4 coalmine in Iran, which will be undergoing stress variation due to future mining activities. The mine consists of 5 seams C1, C2, B1, B2 and D with different characteristics. The results show that the seams C1 and B2 with predicted 94.6% and 81.2% efficiency, have high potential for gas drainage, and considering dip, uniformity and thickness it is suitable to use HF technique. The B1 seam with 31.8 percent efficiency has low potential for gas drainage by HF. HF would not be appropriate for both of C2 and D seams with 7.5 percent efficiency

Stratigraphy and reservoir quality of the turbidite deposits, western sag, Bohai bay, China P.R.

Stratigraphic and subtle reservoirs such as pinchouts, sand lenses and unconformities have been discovered in Bohai basin. These reservoirs occur in sub-basins and sag structures called depressions. A prolific depression is the Liaohe depression that has been filled with rapidly changing mixed alluvial fan deposit of the Cenozoic age. Attempts made at recovering residual hydrocarbon from the subtle reservoir have necessitated the re-evaluation of available data to characterize and model the prolific Shahejie Formation turbidite deposit occurring as pinchouts and sand lenses for hydrocarbon assessment, reservoir quality and possible recovery through enhanced methods. Methods employed covered well logs analysis, clustering analysis for electrofacies and fuzzy logic analysis to predict missing log sections. Stratigraphic and structural analysis was done on SEGY 3D seismic volume after seismic to well tie. Stochastic simulation was done on both discrete and continuous upscaled data. This made it possible to correctly locate and laterally track identified reservoir formation on seismic data. Petrophysical parameters such as porosity and permeability were modeled with result of clustering analysis. Result shows that electrofacies converged on 2 rock classes. The area is characterized by the presence of interbeded sand-shale blanket formations serving as reservoir and seal bodies. The reservoir quality of the formations as seen on the petrophysical analysis done is replicated in simulation volume results. Reservoir rocks have porosity between 0.1 and 0.25, permeability between 1 and 2mD and hydrocarbon saturation as high as 89%. Lithofacies are observed to be laterally inconsistent, sub-parallel to dipping and occurring as porous and permeable continuous beds or pinchouts hosting hydrocarbon. The stochastic stratigraphic model depicts rock units in associations that are synsedimentary. The prevalent configuration gotten from the model gave an insight into exploring and developing the field for enhanced oil recovery of the heavy hydrocarbon of this area