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Abstract In this case study, we apply a novel fracture imaging and interpretation workflow to take a systematic look at hydraulic fractures captured during thorugh fracture coring at the Hydraulic Fracturing Test Site (HFTS) in Midland Basin. Digital fracture maps rendered using high resolution 3D laser scans are analyzed for fracture morphology and roughness. Analysis of hydraulic fracture faces show that the roughness varies systematically in clusters with average cluster separation of approximately 20' along the core. While isolated smooth hydraulic fractures are observed in the dataset, very rough fractures are found to be accompanied by proximal smoother fractures. Roughness distribution also helps understand the effect of stresses on fracture distribution. Locally, fracture roughness seems to vary with fracture orientations indicating possible inter-fracture stress effects. At the scale of stage lengths however, we see evidence of inter-stage stress effects. We also observe fracture morphology being strongly driven by rock properties and changes in lithology. Identified proppant distribution along the cored interval is also correlated with roughness variations and we observe strong positive correlation between proppant concentrations and fracture roughness at the local scale. Finally, based on the observed distribution of hydraulic fracture properties, we propose a conceptual spatio-temporal model of fracture propagation which can help explain the hydraulic fracture roughness distribution and ties in other observations as well.
Summary We collected more than 500 ft of through‐fracture core in the Upper Wolfcamp (UWC) and Middle Wolfcamp (MWC) formations in the Permian Basin. As part of core characterization, we analyzed the core‐sludge samples for the presence of proppant and natural‐calcite particles. Apart from sample preparation and imaging, we designed and developed a novel image‐processing work flow to detect and classify the particles. We used the observations from the identified particle distribution within the stimulated rock volume to understand proppant‐transport behavior. We used relative distributions of smaller 100‐mesh‐ and larger 40/70‐mesh‐proppant particles to interpret proppant placement in relation to perforation clusters. Finally, we used the relative distribution of particles to understand the interaction between natural and hydraulic fractures. We observe that stress variations and the degree of natural fracturing have a bearing on local proppant‐screenout behavior. Smaller 100‐mesh proppant seems to dominate at larger lateral offsets from the hydraulically fractured wells. We also observe indications of heel‐side bias according to lateral proppant distribution. We share our work flow for particle detection and classification, which can serve as a template for proppant analysis in the future if significant through‐fracture cores are collected in similar field experiments.
Abstract The primary aim of this study was to develop robust methods aimed at detecting and quantifying the subsurface proppant distribution as it relates to the completed wells in both the upper and the middle Wolfcamp formations at the HFTS site. There were two sources of proppant deemed useful for this task. The first source was the actual scrapings from the fracture faces that were collected during the core description work. The second sample source was the scrapings and sludge from the cut core tubes collected during core handling. This study highlights the analysis done on the second set of sludge samples. Apart from developing a method for detection and quantification of proppant and other particles contained within this sludge, the study was aimed at the following broad objectives:Determine the spatial distribution of proppant in the created SRV along the cored interval, including size distribution and proppant concentration. Determine if pay zones of interest are sufficiently propped/ stimulated. Determine if fracture and cluster spacing is optimal for thorough lateral reservoir coverage. Determine if well spacing is optimal based on propped SRV length. Introduction A significant part of the HFTS data collection effort was the collection of substantial through fracture cores from both the upper and the middle Wolfcamp formations (UWC & MWC). In total, almost 600 ft. of core was collected using a slant core well. The location of the 11 new horizontal laterals as well as the slant core well is shown in Figure 1. The individual core barrels contain sludge from drilling, coring and core handling operations. The basic premise of our study lies in the assumption that in zones where proppant is present, a significant portion of it should show up within the core barrels. Understanding how this proppant is distributed along the cored section of the slant core well, can be instrumental in understanding fracture communication as well as propped fracture growth along the cored interval of the slant core well. Moreover, the results can then be compared with some of the other independent data available from other datasets collected as part of the HFTS program with the intention of validating or improving our understanding of said data and also helping us with our analysis. The steps followed in our analysis of the proppant are as follows:Weighing, washing, sieving, sub-sampling and various other sample preparation steps before they are imaged at high resolution using a transparency scanner. These images are then run through an automated proppant detection workflow to identify how much proppant and possible natural calcite is within the sampled material. A post picking QC step is also utilized to make sure that the final reported numbers are relatively accurate. Picked objects are segregated to classify size fractions. Post QC numbers are validated at random using direct sample observation under microscope. Two hundred and thirteen core sludge samples scraped from the interior of core barrel sleeves and exterior of the core itself were collected. This extended over all six cored sections as highlighted in Table 1. Each sample is from a 3' core tube barring a few cases where they are smaller. Table 1 displays the core section starting depths, final depths, and number of samples (tubes) recovered from each, etc. Samples varied in volume, weight and consistency. The largest sample received weighed 587g and the smallest sample weighed 7.5g. Samples contain drilling mud, shale particles, aluminum shards (from the process of milling open the core barrels to access the core), natural cement and calcite, other particles and proppant. We note that as part of this study, colored proppant was also pumped to trace the movement of proppant within the SRV. However, tests with autoclave on fresh resin coated proppant showed that under high temperature, resin itself changes color (from reddish to yellowish hue, etc.). Therefore, detection and analysis of colored proppant was not taken up as part of this study.
Summary Digital rock analysis (DRA) embraces multi-scale rock imaging and is becoming a standard tool for reservoir characterization. One of the challenges resides in how to link together rock properties derived from such different scale of magnitude. In this paper we demonstrate that rock properties can be upscaled throughout the nano-scale to core scale by combining DRA, Machine learning (ML) techniques, and high performance computing (HPC) platforms. The approach is based on the understanding that a rock consists of multi-scale rock fabrics intermixed spatially. Here, fabrics refer to complex visual patterns formed by distinct features which properties are extracted using mathematical models. Thus, rock fabrics are captured as groups of patterns within a digital image. These fabrics are linked with rock classes. Rock typing classification is performed on high resolution log data. It is based on simultaneous multi-dimensional cluster analysis within datasets using an appropriated ML technique. Introduction Machine learning (ML) has accelerated advances in many industries. ML brings together multiple disciplines such as computer science, statistics, and natural science to create algorithms that can learn from data. DRA can harness the power of ML to learn from its data, the digital image of rocks, to generate breakthroughs in the oil and gas industry. In this paper, we combine advances in DRA and ML to characterize rock samples at different scales. The framework is based on an understanding that a rock consists of multi-scale rock fabrics intermixed spatially. A rock fabric is defined as a combination of rock features. Similar rock fabrics have similar properties or follow similar property trends. We developed ML algorithms that can automatically learn about rock fabrics and their patterns. These algorithms have the ability to build a model from data without strict instructions. Detailed discussion regarding ML can be found in Bishop, 2006 and Bengio, 2009. Examples of ML-based computer vision applications include autonomous vehicle technology, automatic tumor detection, and object recognition. Digital images produced in DRA can be also considered as data. Based on this perspective, DRA can harness the power of ML to discover and learn from its data.
Summary The Wolfcamp shale is a major oil and gas producing formation in Texas. Oil production from the Permian region is now over 2 million BOPD despite large declines in rig count over the last 12 months (EIA, April, 2016). The Delaware Basin is contributing an increasing share of Permian production. The well analyzed for this project was drilled in 2014 and had a full diameter core sample of about 247 feet in length. Dual-energy CT (DE-CT) scanning and spectral gamma logging was used to compute mineralogy and TOC along the core. Over the same vertical interval, drill cuttings were acquired at a sample interval of 5 ft. Drill cuttings were also acquired at 30 foot intervals along the lateral portion of the wellbore. Wireline log data obtained on this well includes quad combo, spectral GR, image log, elemental components log, and a full wave sonic. This data has been used to compute fluid and volumetric properties as well as rock mechanical properties. Introduction and Goals A major goal was to quantify total porosity (PhiT), effective porosity (PhiE), porosity associated with organic matter (PAOM) from core samples and use this data to aid in interpretation of the wireline log data. The subject well was drilled in Culberson Co., TX and will be called Wolfcamp 1. This well was chosen to serve as a science well and is one of several wells drilled on behalf of the same lease holder. As such, a comprehensive core and well log analysis program was and executed by Halliburton Inc. of Houston, TX and several core analysis, digital rock, and geochemistry service labs. The zone of interest was in the upper Wolfcamp A. This project involved the integration of data from about 247 feet of whole core, drill cuttings sampled every 5 feet over the zone of interest, and a comprehensive suite of open-hole well logs. The log display over the cored interval is shown in Figure 1. In the Wolfcamp, PAOM is interbedded with inter-granular porosity and most of the hydrocarbon resource is likely to be stored in the PAOM. Ion-milled SEM data and 3D FIB-SEM data was used to obtain PAOM, pore size, and permeability from about 30 plug samples. Experimental and Analytical Methods Core and drill cuttings Full diameter core was shipped in sealed aluminum tubes from the well site to the digital rock lab. The core was imaged with a modern medical X-ray CT scanner in dual-energy (DE) mode. The DE-CT scan data was processed to compute bulk density (RHOB) and photoelectric effect (PEF) using a method described by Vinegar, 1986 and Coenen and Maas, 1994. The core RHOB and PEF curves were calibrated using known material standards and it was observed that the core data were generally consistent with the available open-hole well logs. In parallel, the samples were scanned with a spectral gamma logger to measure the natural gamma emissions from thorium, potassium, and uranium.
Unconventional reservoirs present several challenges for petrophysical analysis where uncertainties are often much larger when compared to the usual conventional reservoirs. These low porosity and permeability rocks are associated with complex mineralogical properties and require an integrated approach to better characterize the reservoir, starting from a fit-for-purpose logging suite, the acquisition of high quality core and the implementation of an optimised core analysis program.
In this paper the authors discuss an approach used to de-risk a tight gas play, where they made particular use of NMR log and Dean-Stark analysis for porosity and water saturation model calibration. OBM drilling fluids could not be used due to environmental restrictions consequently Deuterium Oxide (commonly known as D2O) was used to dope the water based drilling mud, to discriminate between the volumes of mud filtrate and formation water within the formation.
The formations intercepted by the boreholes proved to have similarities from a stratigraphic/geological point of view, but showed significant differences when seeking to define producible water volumes using NMR estimates of irreducible water saturation (Swi) and core derived water saturations. In Well#1 resistivity derived water saturations were low and very close to NMR irreducible water saturations suggesting little or no formation water should be produced. In contrast in Well#2, a much more conglomeratic rock, a discrepancy was observed between core derived water saturations and NMR irreducible water saturations suggesting that this formation was not a irreducible water saturation and would likely contain producible water. It was also noteworthy that there existed a salinity variation between the two wells, where in Well #2 for formation water was more “fresh-brackish” then in Well #1.
Well #1 was subsequently fracture stimulated and produced a mixture of gas, condensate and water (assumed to be frac fluid – based on salinity measurement) and well#2 was put on hold based on high water saturation indication and complex conglomeratic rock matrix. Petrophysical analysis played a key role in enabling the business decision through the integration of advanced logging and core analysis.
Abstract This study has been undertaken in two oil fields (A-Libya, and B-Libya) in Sirte basin located in Libya. Nubian sandstone Formation is the main reservoir in the studied oil fields. Laboratory resistivity measurements were performed at Libyan Petroleum institute (LPI). The majority of wells, however, are logged and the use of wireline log data in conjunction with some core data has been proposed as a rapid, cheap, and alternative to predict some special core analysis (SCAL) parameters instead of collecting extensive core or performing SCAL measurements in all wells. Neural network predictors are potentially very useful in the present study due to the limited SCAL data for the studied wells. In this work some of SCAL parameters were predicted using neural networks based on different combinations of wireline logs. The procedure firstly involved training the neural network predictors using data in training well A-02. These predictors were then applied to an adjacent well A-01 in the same oil field, and to another test well B-01 in a different oil field. The most frequently used type of neural network is a feed forward neural network using a back-propagation learning algorithm, due to its popularity and simplicity. Some good neural network SCAL parameter predictors for Rt, and RI were generated using different combinations of standard wireline logs in the training well A-02. The best predictors were produced using the dataset from the entire 478 ft cored interval of the training well and all 7 available wireline logs. Predictors trained on data at 1.0 ft depth spacing appeared to be better in the training well. However, the prediction of resistivity parameters in an adjacent well and a further test well in a different oil field gave slightly better results in general for predictors trained on data at 0.5ft depth spacing rather than at 1.0 ft depth spacing.
Summary The efficacy of crushed-rock samples vs. small plugs or full-diameter core samples for measurement of porosity, permeability, and fluid saturation is an important consideration in the evaluation of tight-gas reservoirs and shale-gas reservoirs. Crushed-rock core analysis methods originally developed for shale reservoirs are now, in some cases, being extended to low-quality tight-gas reservoirs. In this study, crushed-rock and full-diameter core measurements from two wells drilled with oil-based mud are compared to evaluate which of the two core-analysis methods is more reliable for water-saturation assessment of a major North American tight-gas siltstone play (Montney Formation, western Canada). Measurements from the studied full-diameter core samples have wide ranges of water saturation (10 to 45%) and bulk volume water (BVW) (0.5 to 2.6%). In contrast, measurements from crushed-rock samples have much narrower ranges of water saturation (10 to 20%) and BVW (0.2 to 0.7%). The lower values and limited range of water-content measurements from crushed-rock samples suggest a significant degree of artificial water loss during sample handling in the laboratory. This conclusion is supported by comparing core-measured BVW with deep-resistivity values from openhole well logs. Full-diameter BVW measurements correlate well with log resistivity, indicating they are generally representative of in-situ reservoir conditions. Crushed-rock BVW values, on the other hand, show no correlation with log resistivity. The results of this study suggest caution is warranted in the use of crushed-rock samples for water-saturation measurements of siltstones or silty shales. Failure to recognize artificial water loss from crushed-rock siltstone samples could lead to an erroneous interpretation of irreducible water saturation at in-situ reservoir conditions with potentially serious implications for resource evaluation and exploitation.
Abstract The main structural features affecting the Mesozoic sequences of the Gulf rim are a series of interior salt basins extending from south Texas to Alabama. These basins formed during the rifting stage of the formation of the Gulf of Mexico. The early Oxfordian Smackover Formation, characterized by organic-rich, carbonate source rock intervals, represents the initial phase of the Late Jurassic marine transgression in the Gulf of Mexico Basin, where a density-stratified shallow sea was developed. It is predominantly composed of carbonates and calcareous shales and can easily be correlated by its lithology for considerable distances along its depositional strike. Over most of the Gulf coast, it can be subdivided into two distinct units. The Lower Smackover Formation (Brown Dense) is composed of dark-colored, organic-rich carbonate mudstone and dense argillaceous limestone deposited in a low-energy setting, while the Upper Smackover Formation is typically composed of coarser, grain-supported, porous carbonates formed in a high-energy shallow-water environment. Oil and gas resources have been produced from the more porous upper unit on which most geologic works have mainly focused. The Brown Dense has been, however, less studied, although it could become a viable resource play with the utilization of modern drilling techniques. Recently collected 450-ft (ca. 137 m) thick core and full-suite well logs have been studied to delineate geologic features, the results of which would be significant impact on exploration programs. The detailed geologic studies have revealed that 1) the Brown Dense interval comprises the multi-stacked cyclic succession, having the coarsening-upward trend, in which the basal part is dominated with organic-rich deposits, grading upward into algal laminite-like deposits, 2) each coarsening-upward succession (interpreted as parasequence), sharply demarcated by flooding surface, would be deposited during a regression period, 3) the porosity-permeability trend is characterized by organic-rich deposits that have relatively high effective porosity (average > 3%) and permeability (up to 5 µD), respectively 4) the presence of framboidal pyrites is mostly associated with organic-rich layers, where relatively high porosity is evident, 5) porosity is mainly comprised of the shelter type with some amounts of interparticle and intraparticle pores, and 6) cementation might be severe throughout the entire interval, but it would be limited within the organic layers.
Abstract The problem of core-log depth matching/shifting is an integral part of any petrophysical interpretation and modeling. The correct and robust solution to this task predetermines the quality of the resultant petrophysical model however this solution is not straightforward. In this paper the authors present specific algorithms aimed at automated processing, depth shifting and calibration of downhole wireline logs and petrophysical properties measured in core analysis.