砂泥岩储层测井产能预测方法研究

Productivity prediction is very important in the exploration and development of oilfields. Using well log data to predict productivity is a front-line technology， which is key issue in petroleum exploration phase. The essential factors of productivity prediction is building practical models and correcting various causes to improve precision of prediction parameters. Any errors of parameters selections can affect the calculation of productivity prediction; therefore， how to improve research means and calculation accuracy is an important task of productivity prediction. Theory and case examples are deeply and comprehensively studied in the paper. Based on the theory of mud-filtrate invasion and experimental results， the damage of drilling， cementing， perforating，acidizing and fracturing were investigated. The damage depth was quantitatively evaluated by log data， based on this， the processing results of reservoir sensitivity were used to analysis quantitatively the damage of reservoir. The productivity prediction and reservoir damage were initiatively incorporated according to well logging， and the precision of productivity prediction was effectively improved. The method of NMR was explored to calculate the fluid viscosity on the basis of reservoir physical method， and the differences between the two methods were compared in the paper. From the theory fluid flow in porous media， various of theoretical models of production prediction were explored and several practical models were consided， such as productivity index method， improved productivity index method， improved Bearder method， SVM and so on. The characteristic and the application scope of these methods were studied. The inflow productivity and outflow productivity were incorporated and nodal analysis method was used to forecast wellhead yield， thus achieved scientifically production. On the applied background of conventional logging suite， the applying of special items or new logging method which is practical in the research area were studied， the logging suite was further optimized， and the precision of forecast was improved. On the basis of the modeling and the calculation of parameters， these methods were verified and analyzed， and the reconstruct principle was also built for block reservoir. The research block was processed by these methods and compared with testing data. Based on above the research， a technological system which is practical for shaly sand profiles in Shengli Oilfield was built. The system can reach commercialized degree，and satisfied the need of exploration and development of the oilfield.

Identification of lift-off mechanism failure for salt drill-in drilling fluid containing polymer filter cake through adsorption/desorption studies

Drilling fluid`s contact with the productive zone of horizontal or complex wells can reduce well productivity by fluid invasion in the borehole wall. Salted drilling drill-in fluid containing polymers has often been applied in horizontal or complex petroleum wells in the poorly consolidated sandstone reservoirs of the Campos basin, Rio de Janeiro, Brazil. This fluid usually consists of natural polymers such as starch and xanthan gum, which are deposited as a filter cake on the wellbore wall during the drilling. Therefore, the identification of a lift-off mechanism failure, which can be detachment or blistering and pinholing, will enable formulation improvements. increasing the chances of success during filter cake removal in open hole operations. Likewise, knowledge of drill-in drilling fluid adsorption/desorption onto sand can help understand the filter cake-rock adhesion mechanism and consequently filter cake lift-off mechanism failures. The present study aimed to identify the lift-off failure mechanism for this type of fluid filter cake studying adsorption/desorption onto SiO(2) using solutions of natural polymers, lubricants, besides the fluid itself. Ellipsometry was employed to measure this process. The adsorption/desorption studies showed that the adsorbed layer of drilling fluid onto the walls of the rock pores is made up of clusters of polymers, linked by hydrogen bonds, which results in a force of lower cohesion compared to the electrostatic interaction between silica and polymers. Consequently, it was found that the most probable filter cake failure mechanism is rupture (blistering and pinholing), which results in the formation of ducts within the filter cake. (C) 2009 Elsevier B.V. All rights reserved.

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Estimativas da condutividade térmica dos minerais e rochas e influência de parâmetros térmicos e petrofísicos na resistividade aparente da formação

O presente estudo realiza estimativas da condutividade térmica dos principais minerais formadores de rochas, bem como estimativas da condutividade média da fase sólida de cinco litologias básicas (arenitos, calcários, dolomitos, anidritas e litologias argilosas). Alguns modelos térmicos foram comparados entre si, possibilitando a verificação daquele mais apropriado para representar o agregado de minerais e fluidos que compõem as rochas. Os resultados obtidos podem ser aplicados a modelamentos térmicos os mais variados. A metodologia empregada baseia-se em um algoritmo de regressão não-linear denominado de Busca Aleatória Controlada. O comportamento do algoritmo é avaliado para dados sintéticos antes de ser usado em dados reais. O modelo usado na regressão para obter a condutividade térmica dos minerais é o modelo geométrico médio. O método de regressão, usado em cada subconjunto litológico, forneceu os seguintes valores para a condutividade térmica média da fase sólida: arenitos 5,9 ± 1,33 W/mK, calcários 3.1 ± 0.12 W/mK, dolomitos 4.7 ± 0.56 W/mK, anidritas 6.3 ± 0.27 W/mK e para litologias argilosas 3.4 ± 0.48 W/mK. Na sequência, são fornecidas as bases para o estudo da difusão do calor em coordenadas cilíndricas, considerando o efeito de invasão do filtrado da lama na formação, através de uma adaptação da simulação de injeção de poços proveniente das teorias relativas à engenharia de reservatório. Com isto, estimam-se os erros relativos sobre a resistividade aparente assumindo como referência a temperatura original da formação. Nesta etapa do trabalho, faz-se uso do método de diferenças finitas para avaliar a distribuição de temperatura poço-formação. A simulação da invasão é realizada, em coordenadas cilíndricas, através da adaptação da equação de Buckley-Leverett em coordenadas cartesianas. Efeitos como o aparecimento do reboco de lama na parede do poço, gravidade e pressão capilar não são levados em consideração. A partir das distribuições de saturação e temperatura, obtém-se a distribuição radial de resistividade, a qual é convolvida com a resposta radial da ferramenta de indução (transmissor-receptor) resultando na resistividade aparente da formação. Admitindo como referência a temperatura original da formação, são obtidos os erros relativos da resistividade aparente. Através da variação de alguns parâmetros, verifica-se que a porosidade e a saturação original da formação podem ser responsáveis por enormes erros na obtenção da resistividade, principalmente se tais "leituras" forem realizadas logo após a perfuração (MWD). A diferença de temperatura entre poço e formação é a principal causadora de tais erros, indicando que em situações onde esta diferença de temperatura seja grande, perfilagens com ferramentas de indução devam ser realizadas de um a dois dias após a perfuração do poço.

Interstitial water chemistry, Deep Sea Drilling Project, Leg 96

On Leg 96 of the Deep Sea Drilling Project (DSDP), holes were drilled in Orca and Pigmy basins on the northern Gulf of Mexico continental slope and on the Mississippi Fan. The holes on the fan encountered interbedded sand, silt, and mud deposited extremely rapidly, most during late Wisconsin glacial time. Pore-water chemistry in these holes is variable, but does not follow lithologic changes in any simple way. Both Ca and SO4 are enriched in the pore water of many samples from the fan. Two sites drilled in the prominent central channel of the middle fan show rapid SO4 reduction with depth, whereas two nearby sites in overbank deposits show no sulfate reduction for 300 m. Calcium concentration decreases as SO4 is depleted and Li follows the same pattern. Strontium, which like Li, is enriched in samples enriched in Ca, does not decrease with SO4 and Ca. Potassium in the pore water decreases with depth at almost all sites. Sulfate reduction was active at the two basin sites and, as on the fan, this resulted in calcium carbonate precipitation and a lowering of pore water Ca, Mg, and Li. The Orca Basin site was drilled through a brine pool of 258? salinity. Pore-water salinity decreases smoothly with depth to 50 m and remains well above normal seawater values to the bottom of the hole at about 90 m. This suggests constant sedimentation under anoxic hypersaline conditions for at least the last 50,000 yr.

Iodine and boron concentrations in pore fluids, ultramafic clasts and interstitial serpentinite mud of ODP Hole 125-779A and Site 195-1200

Iodine and boron were analyzed in pore fluids, serpentinized ultramafic clasts, and the serpentinized mud matrix of the South Chamorro Seamount mud volcano (Ocean Drilling Program Leg 195 Site 1200) to determine the distribution of these elements in deep forearc settings. Similar analyses of clasts and muds from the Conical Seamount mud volcano (Leg 125 Site 779) were also carried out. Interstitial pore fluids are enriched in boron and iodine without appreciable change in chloride concentration relative to seawater. Both the ultramafic clasts and the associated serpentinized mud present the highest documented iodine concentrations for all types of nonsedimentary rocks (6.3-101.7 µmol/kg). Such high iodine concentrations, if commonplace in marine forearc settings, may constitute a significant, previously unknown reservoir of iodine. This serpentinized forearc mantle reservoir may potentially contribute to the total crustal iodine budget and provide a mechanism for its recycling at convergent plate margins. Both clasts and mud show concurrent enrichments in boron and iodine, and the similarity in pore fluid profiles also suggests that these two incompatible, fluid-mobile elements behave similarly at convergent plate margins.

Stable isotope composition of pore waters from ODP Site 195-1200 serpentinite mud, South Chamorrow Seamount

The South Chamorro Seamount is a serpentinite mud volcano near the southern end of the Mariana forearc. The mud volcano was sampled by drilling during Ocean Drilling Program Leg 195. Samples of pore water squeezed from serpentinite mud were analyzed for stable isotope compositions of carbon in dissolved inorganic carbon and methane, sulfur in sulfate and sulfide, and oxygen in sulfate.

Grain counts and XRD mineralogy of turbidite sand and hemipelagic mud from ODP Leg 168 sites

Sequences of late Pliocene to Holocene sediment lap onto juvenile igneous crust within 20 km of the Juan de Fuca Ridge in northwestern Cascadia Basin, Pacific Ocean. The detrital modes of turbidite sands do not vary significantly within or among sites drilled during Leg 168 of the Ocean Drilling Program. Average values of total quartz, total feldspar, and unstable lithic fragments are Q = 35, F = 35, and L = 30. Average values of monocrystalline quartz, plagioclase, and K-feldspar are Qm = 46, P = 49, and K = 5, and the average detrital modes of polycrystalline quartz, volcanic-rock fragments, and sedimentary-rock plus metamorphic-rock fragments are Qp = 16, Lv = 43, and Lsm = 41. Likely source areas include the Olympic Peninsula and Vancouver Island; sediment transport was focused primarily through the Strait of Juan de Fuca, Juan de Fuca Channel, Vancouver Valley, and Nitinat Valley. Relative abundance of clay minerals (<2-µm-size fraction) fluctuate erratically with depth, stratigraphic age, and sediment type (mud vs. turbidite matrix). Mineral abundance in mud samples are 0%-35% smectite (mean = 8%), 18%-59% illite (mean = 40%), and 29%-78% chlorite + kaolinite (mean = 52%). We attribute the relatively low content of smectite to rapid mechanical weathering of polymictic source terrains, with little or no input of volcanic detritus from the Columbia River. The scatter in clay mineralogy probably was caused by converging of surface currents, turbidity currents, and near-bottom nepheloid clouds from several directions, as well as subtle changes in glacial vs. interglacial weathering products.