SPWLA 61st Annual Online Symposium Transactions最新文献

FORMATION EVALUATION WITH NMR, RESISTIVITY AND PRESSURE DATA – A CASE STUDY OF A CARBONATE OILFIELD OFFSHORE WEST AFRICA 利用核磁共振、电阻率和压力数据进行地层评价——以西非海上某碳酸盐岩油田为例

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-24 DOI: 10.30632/spwla-5011

Tingzi Li, N. Drinkwater, Karen Whittlesey, P. Condon

引用次数: 0

AN INTEGRATED PETROPHYSICAL WORKFLOW FOR FLUID CHARACTERIZATION AND CONTACTS IDENTIFICATION USING NMR CONTINUOUS AND STATIONARY MEASUREMENTS IN HIGH-POROSITY SANDSTONE FORMATION, OFFSHORE NORWAY 在挪威海上的高孔隙度砂岩地层中，利用核磁共振连续和静止测量，实现流体表征和接触面识别的集成岩石物理工作流程

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-24 DOI: 10.30632/spwla-5023

M. Kozłowski, D. Chakraborty, V. Jambunathan, P. Lowrey, R. Balliet, Bob Engelman, K. Ånensen, A. Kotwicki, Y. Johansen

引用次数: 0

IDENTIFYING FRACTURE FILLING MATERIAL IN OIL-BASED MUD WITH DIELECTRIC- BOREHOLE IMAGING 电介质井眼成像识别油基泥浆裂缝充填物

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-24 DOI: 10.30632/spwla-5015

Peter Schlicht, Tianhua Zhang, M. Lüling, B. Graham, Alexandra Cournot, R. Sadownyk

{"title":"IDENTIFYING FRACTURE FILLING MATERIAL IN OIL-BASED MUD WITH DIELECTRIC- BOREHOLE IMAGING","authors":"Peter Schlicht, Tianhua Zhang, M. Lüling, B. Graham, Alexandra Cournot, R. Sadownyk","doi":"10.30632/spwla-5015","DOIUrl":"https://doi.org/10.30632/spwla-5015","url":null,"abstract":"Natural fractures maintain a significant role in many hydrocarbon plays, in both conventional and unconventional reservoirs. In exploration and development scenarios, specific fracture properties, such as orientation and density, are important. However, more critical is their internal architecture: are the fractures open to fluid flow or filled with minerals? Borehole microresistivity imaging tools are widely used to determine these fracture characteristics. In wells drilled with water-based muds, open fractures are filled with conductive borehole fluid that enables distinguishing open, water-filled fractures from resistive, mineral-filled fractures and the surrounding rock. However, many wells today are drilled with oilbased muds. In this case, mineral-filled fractures and oil-basedmud-filled fractures are equally highly resistive and cannot be directly distinguished using resistivity images only. The latest-generation wireline oil-based-mud microresistivity imagers operate in the megahertz frequency range, radiating the electrical current capacitively through the nonconductive mud column and delivering photorealistic borehole images. Both electrical conductivity and dielectric permittivity components constitute the measured signal. The quantitative interpretation uses a sequence of modelbased parametric inversion runs to first estimate the mud properties of the log and subsequently invert for the standoff of the microelectrode buttons to the rock surface and the formation resistivity and dielectric permittivity within the volume of investigation. Our example case shows highly resistive, high-angle fractures from the resistivity images with their orientation and density. The standoff image determines if the mud column penetrates the fracture plane, showing an apparently high standoff compared with the surrounding rock. If the standoff appears high in the fracture plane, the fracture is classified open to fluid flow. However, are these fractures indeed fully dilated and open or are they filled with different materials— are they partially mineralized with calcite and partially open, filled with mud? To further determine the fracture fill and susceptibility to fluid flow, a new workflow employs the material dependency of the relative dielectric permittivity. The relative permittivity is estimated as function of resistivity and frequency pixel by pixel on the resistivity image. The estimate formula is empirically derived from several hundred laboratory measurements on core plugs with different fluid saturations and salinities. The resulting borehole image enables distinguishing materials in the volume of investigation. Bio: Peter SCHLICHT is the Technical Director for Geology at Schlumberger Wireline Headquarters, based in Bucharest, Romania. He joined Schlumberger in 2001 as a wireline field engineer and had several assignments as geology expert to operations in Africa and the Middle East. Peter was research program manager for pre-salt formation e","PeriodicalId":285200,"journal":{"name":"SPWLA 61st Annual Online Symposium Transactions","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125031527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANALYSIS OF LATERAL FLUID GRADIENTS FROM DFA MEASUREMENTS AND SIMULATION OF RESERVOIR FLUID MIXING PROCESSES OVER GEOLOGIC TIME dfa测量的横向流体梯度分析和地质时期储层流体混合过程的模拟

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-24 DOI: 10.30632/spwla-5031

Qing Chen, M. Kristensen, Y. Johansen, V. Achourov, S. Betancourt, O. Mullins

引用次数: 0

MAPPING COMPLEX INJECTITE BODIES WITH MULTI-WELL ELECTROMAGNETIC 3D INVERSION DATA 利用多井电磁三维反演资料填图复杂注入体

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-22 DOI: 10.30632/spwla-5024

N. Clegg, Endre Eriksen, Kevin D. Best, Ingeborg Tøllefsen, Artur Kowicki, Dave Marchant

引用次数: 1

INTEGRATION OF NUCLEAR SPECTROSCOPY TECHNOLOGY AND CORE DATA RESULTS FOR THROUGH CASING TOC MEASUREMENT AND SATURATION ANALYSIS / A CASE STUDY IN NAJMAH-SARJELU RESERVOIR, SOUTH KUWAIT 将核谱技术与岩心数据相结合，进行过套管碳含量测量和饱和度分析/以科威特南部najmah-sarjelu油藏为例

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-22 DOI: 10.30632/spwla-5096

R. Bouchou, A. Abu-Ghneej, M. Ghioca, Nora Alarcon, F. Mendez

引用次数: 1

A COMPARISON AND APPLICATIONS OF THREE DIFFERENT MAXIMUM HORIZONTAL STRESS PREDICTIONS 三种不同最大水平应力预测的比较及应用

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-22 DOI: 10.30632/spwla-5064

E. Najm, J. Quirein

引用次数: 1

EVALUATING PETROPHYSICAL PROPERTIES AND VOLUMETRICS UNCERTAINTIES OF SAND INJECTITE RESERVOIRS – NORWEGIAN NORTH SEA 挪威北海注砂储层岩石物性及体积不确定性评价

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-22 DOI: 10.30632/spwla-5057

A. Kotwicki, AkerBP Asa, Mirza Hassan Baig, Y. Johansen, G. Leirdal, B. Aftret, O. Sandstad, A. Anthonsen, Bruis Gianotten, T. Hansen, M. Firinu, Schlumberger, Vår Energi As

{"title":"EVALUATING PETROPHYSICAL PROPERTIES AND VOLUMETRICS UNCERTAINTIES OF SAND INJECTITE RESERVOIRS – NORWEGIAN NORTH SEA","authors":"A. Kotwicki, AkerBP Asa, Mirza Hassan Baig, Y. Johansen, G. Leirdal, B. Aftret, O. Sandstad, A. Anthonsen, Bruis Gianotten, T. Hansen, M. Firinu, Schlumberger, Vår Energi As","doi":"10.30632/spwla-5057","DOIUrl":"https://doi.org/10.30632/spwla-5057","url":null,"abstract":"Sand injectites on the Norwegian Continental Shelf have proven their commercial significance. Some are already producing, e.g., Volund, Viper, Balder, Ringhorne, and Kobra Fields, while others such as in production licenses (PL) 340 and 869 have recently been discovered and appraised. Extensive literature on the geology of sand injectites has been published (e.g., Jenssen et al., 1993; Jolly and Lonergan, 2002; Huuse et al., 2003; Hurst et al., 2005). However, few references are available on the petrophysical and geophysical aspects of sand injectite reservoirs. This paper discusses the petrophysical properties of sand injectite facies, dykes, sills, and brecciated sands, along with their identification from seismic data. A perception that volumetrics of sand injectite reservoirs cannot be reliably evaluated is assessed. Sand injectites in PL 340 and 869 were interpreted as remobilized sands from the Hermod and Heimdal Formations of Paleocene age injected into the overlying Balder Formation and Hordaland Group mudstones of Eocene age. The mudstones acted as a seal, forming an intrusive stratigraphic trap. The trap geometry varied locally depending upon the dyke and sill geometries of the sandstone. Dykes had large vertical reach with the corresponding high-hydrocarbon column, while sills had low-vertical relief with large lateral extent. Intervals of brecciated sands were also observed within the injectite complex, especially where sands were thin. These brecciated sands contained large amounts of angular mudstone clasts of different dimensions suspended in an overall sandy matrix. Close examination of cored dykes made it possible to observe this, while it might not be as obvious when looking at bulk well logs. Petrophysical-log responses for clean sills and dykes behaved the same way as they would in a clean sandstone reservoir. If sills and dykes were very thin, they would also risk not being counted as net or pay (Suau et al., 1984; Dromgoole et al., 2000; Flølo et al., 2000;). Such errors can impact in-place volumes in a significant way. Sills appeared as blocky clean sand on logs, but it was difficult to differentiate a dyke from a sill or thin sands using logs. Dykes are high-angle features and are identified either by core studies or borehole images when intersected by a well or, if large enough, observable on seismic. Brecciated sand intervals appeared with cm-to-dm-scale mudstone clasts suspended in sand with approximately 40 to 60% net to gross. Log responses over these intervals indicated shaly sand or thin sands. Resistivity and thermal neutron porosity logs were highly affected by the shale clasts. For this reason, a fractional net/gross interpretation technique was used to evaluate the sand content and hydrocarbon pore volume. To further verify these results, they were compared to observations directly on the core. To qualify to what extent petrophysical logs and interpreted products thereof can be relied on to evaluate hydrocarbon ","PeriodicalId":285200,"journal":{"name":"SPWLA 61st Annual Online Symposium Transactions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130929110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

THE BENEFITS AND DANGERS OF USING ARTIFICIAL INTELLIGENCE IN PETROPHYSICS 在岩石物理学中使用人工智能的好处和危险

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-22 DOI: 10.30632/spwla-5066

S. Cuddy

{"title":"THE BENEFITS AND DANGERS OF USING ARTIFICIAL INTELLIGENCE IN PETROPHYSICS","authors":"S. Cuddy","doi":"10.30632/spwla-5066","DOIUrl":"https://doi.org/10.30632/spwla-5066","url":null,"abstract":"Abstract Artificial Intelligence, or AI, is a method of data analysis that learns from data, identify patterns and makes predictions with the minimal human intervention. AI is bringing many benefits to petrophysical evaluation. Using case studies, this paper describes several successful applications. The future of AI has even more potential. However, if used carelessly there are potentially grave consequences. A complex Middle East Carbonate field needed a bespoke shaly water saturation equation. AI was used to ‘evolve’ an ideal equation, together with field specific saturation and cementation exponents. One UKCS gas field had an ‘oil problem’. Here, AI was used to unlock the hidden fluid information in the NMR T1 and T2 spectra and successfully differentiate oil and gas zones in real time. A North Sea field with 30 wells had shear velocity data (Vs) in only 4 wells. Vs was required for reservoir modelling and well bore stability prediction. AI was used to predict Vs in all 30 wells. Incorporating high vertical resolution data, the Vs predictions were even better than the recorded logs. As it is not economic to take core data on every well, AI is used to discover the relationships between logs, core, litho-facies and permeability in multi-dimensional data space. As a consequence, all wells in a field were populated with these data to build a robust reservoir model. In addition, the AI predicted data upscaled correctly unlike many conventional techniques. AI gives impressive results when automatically log quality controlling (LQC) and repairing electrical logs for bad hole and sections of missing data. AI doesn’t require prior knowledge of the petrophysical response equations and is self-calibrating. There are no parameters to pick or cross-plots to make. There is very little user intervention and AI avoids the problem of ‘garbage in, garbage out’ (GIGO), by ignoring noise and outliers. AI programs work with an unlimited number of electrical logs, core and gas chromatography data; and don’t ‘fall-over’ if some of those inputs are missing. AI programs currently being developed include ones where their machine code evolves using similar rules used by life’s DNA code. These AI programs pose considerable dangers far beyond the oil industry as described in this paper. A ‘risk assessment’ is essential on all AI programs so that all hazards and risk factors, that could cause harm, are identified and mitigated.","PeriodicalId":285200,"journal":{"name":"SPWLA 61st Annual Online Symposium Transactions","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129253767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

INTEGRATED RESERVOIR CHARACTERIZATION USING UNSUPERVISED LEARNING ON NUCLEAR MAGNETIC RESONANCE (NMR) T1-T2 LOGS 利用核磁共振(nmr) t1-t2测井数据进行无监督学习的油藏综合表征

SPWLA 61st Annual Online Symposium Transactions Pub Date : 2020-06-22 DOI: 10.30632/spwla-5047

Tianmin Jiang, R. Bonnie, T. S. Correa, Martin C. Krueger, Shaina Kelly, M. Wasson

{"title":"INTEGRATED RESERVOIR CHARACTERIZATION USING UNSUPERVISED LEARNING ON NUCLEAR MAGNETIC RESONANCE (NMR) T1-T2 LOGS","authors":"Tianmin Jiang, R. Bonnie, T. S. Correa, Martin C. Krueger, Shaina Kelly, M. Wasson","doi":"10.30632/spwla-5047","DOIUrl":"https://doi.org/10.30632/spwla-5047","url":null,"abstract":"A novel interpretation workflow was developed using an automated unsupervised learning algorithm on nuclear magnetic resonance (NMR) T1-T2 log data to quantify fluid-filled porosity and saturation, producible oil volumes, and to characterize matrix pore sizes and formation wettability. Core porosity and saturation measurements, scanning electron microscope images (SEM), Rock-Eval pyrolysis, wettability measurements, and mercury injection capillary pressure (MICP) tests are compared with the NMR interpretation for calibration and validation. Understanding in-situ fluid types and volumetrics is key for reservoir characterization. The traditional static formation evaluation model based on triple-combo logs (density, neutron, resistivity, and gamma ray) has been widely used to characterize formations to provide cost-effective answers of lithology, total porosity, and water saturation. Nevertheless, the dynamic result from production often shows quite a different water cut than total water saturation because the static model cannot distinguish immobile hydrocarbons from producible oil. NMR log data show unique signatures of formation fluids, such as gas, immobile hydrocarbon, producible oil, T1-T2 immobile, and free water. The NMR data also provide a method to interpret fluid and matrix properties, including fluid viscosity, pore geometry, and fluid-pore interaction. However, due to the downhole environment and the resolution limitation of the logging tool, the signatures of the fluids are not always well separated. It is challenging to visually separate the signal contributions of different formation fluids on T1-T2 maps. An automated unsupervised learning algorithm based on non-negative matrix factorization (NMF) and hierarchical clustering (Venkataramanan et al., 2018) is implemented in the new workflow to separate T1-T2 signatures of different pore fluids, enabling fluid typing and providing quantitative fluid-filled porosities and associated saturations. T1-T2 signatures of separated fluids are used to characterize fluid mobility, pore sizes, and formation wettability. The new approach is successfully applied to multiple wells for a field case study to characterize the saturation and producibility of hydrocarbon and water, which routine petrophysical models are unable to distinguish. Results are corroborated with dynamic production data showing high free water and high residual oil. This is also validated by routine and special core analyses. Integration of NMR, MICP, and SEM gives pore-body and pore-throat-size distributions with body-to-throat ratio (BTR), increasing the precision of estimated formation permeability. A high T1/T2 ratio of the oil suggests that the formation is partially oil-wet. The wettability results from NMR are consistent with the core wettability test and production results. Understanding which portion of a reservoir contains mobile fluids impacts target zone selection and reserves estimation.","PeriodicalId":285200,"journal":{"name":"SPWLA 61st Annual Online Symposium Transactions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124334503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0