AUTO-NAVIGATION ON PRESSURE AND SAMPLING LOCATION IN WIRELINE AND LWD: BIG DATA CHALLENGES AND SOLUTIONS

2021 ◽  
Author(s):  
Mehdi Alipour K ◽  
◽  
Bin Dai ◽  
Jimmy Price ◽  
Christopher Michaell Jones ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Expert Knowledge ◽  
Sampling Location ◽  
Neutron Density ◽  
Reserve Estimation ◽  
Pressure Test ◽  
Single Well ◽  
Application Deployment ◽  
Exploratory Data ◽  
Petrophysical Logs

Measuring formation pressure and collecting representative samples are the essential tasks of formation testing operations. Where, when and how to measure pressure or collect samples are critical questions which must be addressed in order to complete any job successfully. Formation testing data has a crucial role in reserve estimation especially at the stage of field exploration and appraisal, but can be time consuming and expensive. Optimum location has a major impact on both the time spent performing and the success of pressure testing and sampling. Success and optimization of rig-time paradoxically requires careful and extensive but also quick pre-job planning. The current practice of finding optimum locations for testing heavily rely on expert knowledge. With nearly complete digitization of data collection, the oil industry is now dealing with massive data flow giving rise to the question of its application and the necessity to collect. Some data may be so called “dark data” of which a very tiny portion is used for decision making. For instance, a variety of petrophysical logs may be collected in a single well to provide measures of formation properties. The logs may include conventional gamma ray, neutron, density, caliper, resistivity or more advanced tools such as high-resolution image logs, acoustic, or NMR. These data can be integrated to help decide where to pressure test and sample, however, this effort is nearly exclusively driven by experts and is manpower intensive. In this paper we present a workflow to gather, process and analyze conventional log data in order to optimize formation testing operations. The data is from an enormous geographic distribution of wells. Tremendous effort has been performed to extract, transform and load (ETL) the data into a usable format. Stored files contains multi-million to multi-billions rows of data thereby creating technology challenges in terms of reading, processing and analyzing in a timely manner for pre-job planning. We address the technological challenges by deploying cutting-edge data technology to solve this problem. Upon completion of the workflow we have been able to build a scalable petrophysical interpretation log platform which can be easily utilized for machine learning and application deployment. This type of data base is invaluable asset especially in places where there is a need for knowledge of analogous wells. Exploratory data analysis on worldwide data on mobility and some key influencing features on pressure test and sampling quality, is performed and presented. We further show how this data is integrated and analyzed in order to automate selection of locations for which to formation test.

scholarly journals Facies Analysis and Depositoinal Environment of D-3 Reservoir Sands Vin Field, Eastern Niger Delta

2019 ◽  
pp. 1-19
Author(s):  
Onyewuchi, Chinedu Vin ◽  
Minapuye, I. Odigi
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Depositional Environment ◽  
Gamma Ray ◽  
Facies Analysis ◽  
Depositional Environments ◽  
Width Ratio ◽  
Neutron Density ◽  
Wireline Logs ◽  
Isopach Map

Facies analysis and depositional environment identification of the Vin field was evaluated through the integration and comparison of results from wireline logs, core analysis, seismic data, ditch cutting samples and petrophysical parameters. Well log suites from 22 wells comprising gamma ray, resistivity, neutron, density, seismic data, and ditch cutting samples were obtained and analyzed. Prediction of depositional environment was made through the usage of wireline log shapes of facies combined with result from cores and ditch cuttings sample description. The aims of this study were to identify the facies and depositional environments of the D-3 reservoir sand in the Vin field. Two sets of correlations were made on the E-W trend to validate the reservoir top and base while the isopach map was used to establish the reservoir continuity. Facies analysis was carried out to identify the various depositional environments. The result showed that the reservoir is an elongate , four way dip closed roll over anticline associated with an E-W trending growth fault and contains two structural high separated by a saddle. The offshore bar unit is an elongate sand body with length: width ratio of >3:1 and is aligned parallel to the coast-line. Analysis of the gamma ray logs indicated that four log facies were recognized in all the wells used for the study. These include: Funnel-shaped (coarsening upward sequences), bell-shaped or fining upward sequences, the bow shape and irregular shape. Based on these categories of facies, the depositional environments were interpreted as deltaic distributaries, regressive barrier bars, reworked offshore bars and shallow marine. Analysis of the wireline logs and their core/ditch cuttings description has led to the conclusion that the reservoir sandstones of the Agbada Formation in the Vin field of the eastern Niger Delta is predominantly marine deltaic sequence, strongly influenced by clastic output from the Niger Delta. Deposition occurred in a variety of littoral and neritic environment ranging from barrier sand complex to fully marine outer shelf mudstones.

scholarly journals Petrophysical Properties and Digenetic Evolution of Shuaiba Formation in Nasiriyah Oil Field, Southern Iraq

2021 ◽  
pp. 4810-4818
Author(s):  
Marwah H. Khudhair
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Middle Part ◽  
Effective Porosity ◽  
Oil Field ◽  
Neutron Density ◽  
Petrophysical Properties ◽  
Secondary Porosity ◽  
Primary Porosity ◽  
Gamma Ray Log

     Shuaiba Formation is a carbonate succession deposited within Aptian Sequences. This research deals with the petrophysical and reservoir characterizations characteristics of the interval of interest in five wells of the Nasiriyah oil field. The petrophysical properties were determined by using different types of well logs, such as electric logs (LLS, LLD, MFSL), porosity logs (neutron, density, sonic), as well as gamma ray log. The studied sequence was mostly affected by dolomitization, which changed the lithology of the formation to dolostone and enhanced the secondary porosity that replaced the primary porosity. Depending on gamma ray log response and the shale volume, the formation is classified into three zones. These zones are A, B, and C, each can be split into three rock intervals in respect to the bulk porosity measurements. The resulted porosity intervals are: (I) High to medium effective porosity, (II) High to medium inactive porosity, and (III) Low or non-porosity intervals. In relevance to porosity, resistivity, and water saturation points of view, there are two main reservoir horizon intervals within Shuaiba Formation. Both horizons appear in the middle part of the formation, being located within the wells Ns-1, 2, and 3. These intervals are attributed to high to medium effective porosity, low shale content, and high values of the deep resistivity logs. The second horizon appears clearly in Ns-2 well only.

PREDICTION AND ANALYSIS OF GEOMECHANICAL PROPERTIES OF JIMUSAER SHALE USING A MACHINE LEARNING APPROACH

2021 ◽  
Author(s):  
Lianteng Song ◽  
◽  
Zhonghua Liu ◽  
Chaoliu Li ◽  
Congqian Ning ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Gamma Ray ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sequential Data ◽  
Log Data ◽  
Geomechanical Properties ◽  
Single Well

Geomechanical properties are essential for safe drilling, successful completion, and exploration of both conven-tional and unconventional reservoirs, e.g. deep shale gas and shale oil. Typically, these properties could be calcu-lated from sonic logs. However, in shale reservoirs, it is time-consuming and challenging to obtain reliable log-ging data due to borehole complexity and lacking of in-formation, which often results in log deficiency and high recovery cost of incomplete datasets. In this work, we propose the bidirectional long short-term memory (BiL-STM) which is a supervised neural network algorithm that has been widely used in sequential data-based pre-diction to estimate geomechanical parameters. The pre-diction from log data can be conducted from two differ-ent aspects. 1) Single-Well prediction, the log data from a single well is divided into training data and testing data for cross validation; 2) Cross-Well prediction, a group of wells from the same geographical region are divided into training set and testing set for cross validation, as well. The logs used in this work were collected from 11 wells from Jimusaer Shale, which includes gamma ray, bulk density, resistivity, and etc. We employed 5 vari-ous machine learning algorithms for comparison, among which BiLSTM showed the best performance with an R-squared of more than 90% and an RMSE of less than 10. The predicted results can be directly used to calcu-late geomechanical properties, of which accuracy is also improved in contrast to conventional methods.

Gamma-ray, Spectral Gamma-ray, and Neutron-density Logs for Interpretation of Ordovician Volcanic Rocks, West Cumbria, England

2002 ◽  
Author(s):  
David Millward ◽  
Simon R. Young ◽  
Brett Beddoe-Stephens ◽  
Emrys R. Phillips ◽  
Chris J. Evans
Keyword(s):  
Gamma Ray ◽  
Volcanic Rocks ◽  
Neutron Density

Joint petrophysical interpretation of multidetector nuclear measurements in the presence of invasion, shoulder-bed, and well-deviation effects

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. D13-D30 ◽  
Author(s):  
Edwin Ortega ◽  
Mathilde Luycx ◽  
Carlos Torres-Verdín ◽  
William E. Preeg
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Total Porosity ◽  
Neutron Density ◽  
Layer By Layer ◽  
Fluid Type ◽  
Fluid Properties ◽  
Radial Length ◽  
Neutron Porosity ◽  
Porosity Logs

Recent advances in logging-while-drilling sigma measurements include three-detector thermal-neutron and gamma-ray decay measurements with different radial sensitivities to assess the presence of invasion. We have developed an inversion-based work flow for the joint interpretation of multidetector neutron, density, and sigma logs to reduce invasion, shoulder-bed, and well-deviation effects in the estimation of porosity, water saturation, and hydrocarbon type, whenever the invasion is shallow. The procedure begins with a correction for matrix and fluid effects on neutron and density-porosity logs to estimate porosity. Multidetector time decays are then used to assess the radial length of the invasion and estimate the virgin-zone sigma while simultaneously reducing shoulder-bed and well-deviation effects. Density and neutron porosity logs are corrected for invasion and shoulder-bed effects using two-detector density and neutron measurements with the output from the time-decay (sigma) inversion. The final step invokes a nuclear solver in which corrected sigma, inverse of migration length, and density in the virgin zone are used to estimate water saturation and fluid type. The fluid type is assessed with a flash calculation and Schlumberger’s Nuclear Parameter calculation code to account for the nuclear properties of different types of hydrocarbon and water as a function of pressure, temperature, and salinity. Results indicate that accounting for invasion effects is necessary when using density and neutron logs for petrophysical interpretation beyond the calculation of total porosity. Synthetic and field examples indicate that the mitigation of invasion effects becomes important in the case of salty mud filtrate invading gas-bearing formations. The advantage of the developed inversion-based interpretation method is its ability to estimate layer-by-layer petrophysical, compositional, and fluid properties that honor multiple nuclear measurements, their tool physics, and their associated borehole geometrical and environmental effects.

New Analytical Method To Calculate Matrix- and Fluid-Corrected Total Porosity in Organic Shale

2015 ◽  
Vol 18 (04) ◽  
pp. 609-623
Author(s):  
Edwin Ortega ◽  
Carlos Torres-Verdín
Keyword(s):  
Analytical Method ◽  
Gamma Ray ◽  
Total Porosity ◽  
Matrix Composition ◽  
Neutron Density ◽  
Fluid Density ◽  
Remarkable Feature ◽  
Gamma Ray Spectroscopy ◽  
Porosity Logs ◽  
Organic Shale

Summary Estimation of total porosity from neutron and density porosity logs in organic shale (source rock) is challenging because these logs are substantially affected by fluid and matrix-composition effects. Conventional interpretation of neutron and density porosity logs often includes corrections for shale concentration in which the main objective is to improve the calculation of nonshale porosity in hydrocarbon-bearing zones. These corrections are not desirable in unconventional rock formations because shale pores can be hydrocarbon-saturated. Neutron and density porosity readings across shale zones are sometimes averaged by use of the root-mean-square (RMS) method. We introduce a new and simple analytical expression for total porosity that effectively separates both matrix and fluid effects on neutron and density porosity logs. The expression stems from a new nonlinear mixing law for neutron migration length that is coupled with the linear-density mixing law to calculate total porosity and fluid density. The method is applied in two sequential steps: First, separate corrections for only matrix effects are implemented to enhance the neutron-density crossover for qualitative interpretation of fluid type; second, the coupled equation is used to estimate fluid density and actual porosity devoid of matrix and fluid effects. Calculated porosity and fluid density can be used further to calculate water saturation from density logs. One remarkable feature of this method is the ease with which it can be applied to obtain accurate and reliable results. Application of the method only requires knowledge of single-component nuclear properties and mineral volumetric concentrations. One can obtain nuclear properties from a set of charts for multiple fluid types and minerals provided in this paper, whereas one can calculate mineral concentrations on the basis of available triple combo logs or gamma ray spectroscopy logs. Two synthetic and four field examples (two conventional and two shale-gas reservoirs) are used to test the method. First, we describe an application in a conventional siliciclastic sedimentary sequence in which only shale concentration calculated from gamma ray logs is required to improve the estimation of porosity in shaly sections. Second, we document several applications in which gamma ray spectroscopy logs are used together with a reliable hypothesis for clay type to define mineral properties. Results compare well with nuclear-magnetic-resonance (NMR) and core measurements, whereas the new method outperforms the conventional RMS procedure, especially in the cases of gas-bearing, low-porosity organic shale. The new analytical method can be readily implemented on an Excel spreadsheet and requires minimal adjustments for its operation.

Interpretation of porosity and fluid constituents from well logs using an interactive neutron-density matrix scale

2013 ◽  
Vol 1 (2) ◽  
pp. T143-T155 ◽  
Author(s):  
Olabode Ijasan ◽  
Carlos Torres-Verdín ◽  
William E. Preeg
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Matrix Effects ◽  
Neutron Density ◽  
Reservoir Capacity ◽  
Constant Matrix ◽  
Light Oil ◽  
Saturated Formations ◽  
Neutron Porosity ◽  
Interactive Variable

Neutron and density logs are important borehole measurements for estimating reservoir capacity and inferring saturating fluids. The neutron log, measuring the hydrogen index, is commonly expressed in apparent water-filled porosity units assuming a constant matrix lithology whereby it is not always representative of actual pore fluid. By contrast, a lithology-independent porosity calculation from nuclear magnetic resonance (NMR) and/or core measurements provides reliable evaluations of reservoir capacity. In practice, not all wells include core or NMR measurements. We discovered an interpretation workflow wherein formation porosity and hydrocarbon constituents can be estimated from density and neutron logs using an interactive, variable matrix scale specifically suited for the precalculated matrix density. First, we estimated matrix components from combinations of nuclear logs (photoelectric factor, spontaneous gamma ray, neutron, and density) using Schlumberger’s nuclear parameter calculator (SNUPAR) as a matrix compositional solver while assuming freshwater-filled formations. The combined effects of grain density, volumetric concentration of shale, matrix hydrogen, and neutron lithology units define an interactive matrix scale for correction of neutron porosity. Under updated matrix conditions, the resulting neutron-density crossover can only be attributed to pore volume and saturating fluid effects. Second, porosity, connate-water saturation, and hydrocarbon density are calculated from the discrepancy between corrected neutron and density logs using SNUPAR and Archie’s water saturation equation, thereby eliminating the assumption of freshwater saturation. With matrix effects eliminated from the neutron-density overlay, gas- or light-oil-saturated formations exhibiting the characteristic gas neutron-density crossover become representative of saturating hydrocarbons. This behavior gives a clear qualitative distinction between hydrocarbon-saturated and nonviable depth zones.

scholarly journals Characterization of Mishrif Formation Reservoir in Amara Oil Field, Southeast Iraq, Using Geophysical Well-logging

2021 ◽  
pp. 4702-4711
Author(s):  
Asmaa Talal Fadel ◽  
Madhat E. Nasser
Keyword(s):  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Total Porosity ◽  
Effective Porosity ◽  
Oil Field ◽  
Neutron Density ◽  
Reservoir Water ◽  
Resistivity Logs ◽  
Reliable Knowledge

     Reservoir characterization requires reliable knowledge of certain fundamental properties of the reservoir. These properties can be defined or at least inferred by log measurements, including porosity, resistivity, volume of shale, lithology, water saturation, and permeability of oil or gas. The current research is an estimate of the reservoir characteristics of Mishrif Formation in Amara Oil Field, particularly well AM-1, in south eastern Iraq. Mishrif Formation (Cenomanin-Early Touronin) is considered as the prime reservoir in Amara Oil Field. The Formation is divided into three reservoir units (MA, MB, MC). The unit MB is divided into two secondary units (MB1, MB2) while the unit MC is also divided into two secondary units (MC1, MC2). Using Geoframe software, the available well log images (sonic, density, neutron, gamma ray, spontaneous potential, and resistivity logs) were digitized and updated. Petrophysical properties, such as porosity, saturation of water, saturation of hydrocarbon, etc. were calculated and explained. The total porosity was measured using the density and neutron log, and then corrected to measure the effective porosity by the volume content of clay. Neutron -density cross-plot showed that Mishrif Formation lithology consists predominantly of limestone. The reservoir water resistivity (Rw) values of the Formation were calculated using Pickett-Plot method.   

Automated gamma-ray log pattern alignment and depth matching by machine learning

2020 ◽  
Vol 8 (3) ◽  
pp. SL25-SL34
Author(s):  
Shirui Wang ◽  
Qiuyang Shen ◽  
Xuqing Wu ◽  
Jiefu Chen
Keyword(s):  
Machine Learning ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
Machine Learning Techniques ◽  
Matching Task ◽  
Learning Techniques ◽  
Single Well ◽  
Matching Strategy ◽  
Well Log Analysis ◽  
Gamma Ray Log

Depth matching of multiple logging curves is essential to any well evaluation or reservoir characterization. Depth matching can be applied to various measurements of a single well or multiple log curves from multiple wells within the same field. Because many drilling advisory projects have been launched to digitalize the well-log analysis, accurate depth matching becomes an important factor in improving well evaluation, production, and recovery. It is a challenge, though, to align the log curves from multiple wells due to the unpredictable structure of the geologic formations. We have conducted a study on the alignment of multiple gamma-ray well logs by using the state-of-the-art machine-learning techniques. Our objective is to automate the depth-matching task with minimum human intervention. We have developed a novel multitask learning approach by using a deep neural network to optimize the depth-matching strategy that correlates multiple gamma-ray logs in the same field. Our approach can be extended to other applications as well, such as automatic formation top labeling for an ongoing well given a reference well.

Discriminating Shale Layers by Pseudo CGR Logs Created Using Artificial Intelligence

2021 ◽  
Author(s):  
Saud K. Aldajani ◽  
Saud F. Alotaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Gamma Ray ◽  
Effective Porosity ◽  
Carbonate Reservoir ◽  
Added Value ◽  
Subtractive Clustering ◽  
Neutron Density ◽  
High Porosity ◽  
Inference System ◽  
Porosity Logs

Abstract The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.

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