Intelligent Well Drainage and Interference Density Analysis Tool for Optimizing Infill Drilling

2021 ◽  
Author(s):  
Amal Al-Sane ◽  
Mohammad A. Al-Bahar ◽  
Anup Bora ◽  
Prashant S. Dhote ◽  
Gopi Nalla ◽  
...  
Keyword(s):  
Cumulative Distribution ◽  
Analysis Tool ◽  
Reservoir Properties ◽  
Progressive Development ◽  
Drainage Radius ◽  
Well Spacing ◽  
Stock Tank ◽  
Infill Drilling ◽  
Effective Drainage ◽  
Bypassed Oil

Abstract During the progressive development of mature fields, it is imperative to drill many infill wells to accelerate production and access bypassed oil. Optimizing the infill well spacing is always the concern to reduce interference with existing wells and improve recovery. In the present study, using intelligent data mining techniques, a new analysis and visualization tool has been developed and implemented to estimate and map drainage radius by well to assess the efficiency of the current development pattern and properly plan future wells. The tool deployed several performance-based techniques to estimate the contacted stock-tank oil initially in place (STOIIP) by each existing well, and outcomes can be compared between techniques for validation. The contacted STOIIP is then converted into an effective drainage radius by well using reservoir properties from the geo-cellular model. The evaluated reservoir is subdivided vertically into pay zones drained by the wells based on geological barriers/baffles to flow and connectivity across the zones. The tool estimates drainage radii of the wells produced from the reservoir using five different methods. The resultant Proved Developed Producing (PDP) reserves polygon maps are generated for the connected zones. The drainage radii of wells with behind-casing opportunities are estimated based on correlation and adjacent wells methods, and Proved Developed Non-Producing (PDNP) reserves polygon maps were generated. Well interference density is estimated based on overlapping drainage radii polygons with adjacent well locations, which has then been validated with production and pressure data from the wells. This paper describes the methodology by which the well drainage radii and well interference density can be estimated and implemented on a selected reservoir. This workflow can be successfully used to identify drained and undrained areas around the wellbore and opportunities for additional infill wells in the various pay zones of the reservoir. This exercise observed consistency in the drainage radii cumulative distribution from decline curve analysis methods and the No-Further-Activities (NFA) simulation case.

Anisotropy Gas Reservoir Drainage Radius and Quantitative Well Interference Study

2013 ◽  
Vol 275-277 ◽  
pp. 1285-1291 ◽  
Author(s):  
Zheng Long Gao ◽  
Hong Fu Fan ◽  
Zhi Bin Gao
Keyword(s):  
Production Rate ◽  
Major Axis ◽  
Productivity Analysis ◽  
Analysis Method ◽  
Equivalent Radius ◽  
Average Value ◽  
Drainage Radius ◽  
Well Spacing ◽  
Cumulative Production ◽  
The Difference

Unstable productivity analysis method was used to obtain the equivalent radius of 77 wells and the result shows that the equivalent radius ranges from 30 to 970m with an average value of 230m in McKittrick Hills. The difference range of the radius is mainly caused by varying formation properties, gas saturation, production time, etc. Permeability anisotropy changes the drainage from round to ellipse. The major axis and the minor axis of the ellipse are determined by the ratio of major and minor permeability. Current pressure distribution was obtained and was found to be consistent with the modified drainage results, which demonstrates that the unstable productivity analysis method is applicable in the study of gas well drainage radius. An interference well and an observation well’s model was constructed to study well interference quantitatively. When the well spacing is larger than 750m, the productivity will be reduced by 20%. The production rate of interference well is more sensitive to the cumulative production of observation well, when the production rate of interference well is below 16.8×104m3/d.

Integrated workflow approach to static modeling of Igloo R3 reservoir, onshore Niger Delta, Nigeria

2015 ◽  
Vol 3 (3) ◽  
pp. SZ1-SZ14 ◽  
Author(s):  
Emmanuel Kenechukwu Anakwuba ◽  
Clement Udenna Onyekwelu ◽  
Augustine Ifeanyi Chinwuko
Keyword(s):  
Correlation Coefficient ◽  
Acoustic Impedance ◽  
Niger Delta ◽  
Seismic Inversion ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Sequential Gaussian Simulation ◽  
Reservoir Properties ◽  
Static Modeling ◽  
Geophysical Well Logs

We constructed a 3D static model of the R3 reservoir at the Igloo Field, Onshore Niger Delta, by integrating the 3D seismic volume, geophysical well logs, and core petrophysical data. In this model, we used a combined petrophysical-based reservoir zonation and geostatistical inversion of seismic attributes to reduce vertical upscaling problems and improve the estimation of reservoir properties between wells. The reservoir structural framework was interpreted to consist of three major synthetic faults; two of them formed northern and southern boundaries of the field, whereas the other one separated the field into two hydrocarbon compartments. These compartments were pillar gridded into 39,396 cells using a [Formula: see text] dimension over an area of [Formula: see text]. Analysis of the field petrophysical distribution showed an average of 21% porosity, 34% volume of shale, and 680-mD permeability. Eleven flow units delineated from a stratigraphic modified Lorenz plot were used to define the reservoir’s stratigraphic framework. The calibration of acoustic impedance using sonic- and density-log porosity showed a 0.88 correlation coefficient; this formed the basis for the geostatistic seismic inversion process. The acoustic impedance was transformed into reservoir parameters using a sequential Gaussian simulation algorithm with collocated cokriging and variogram models. Ten equiprobable acoustic impedance models were generated and further converted into porosity models by using their bivariate relationship. We modeled the permeability with a single transform of core porosity with a correlation coefficient of 0.86. We compared an alternative model of porosity without seismic as a secondary control, and the result showed differences in their spatial distributions, which was a major control to fluid flow. However, there were similarities in their probability distribution functions and cumulative distribution functions.

TIRRAWARRA AND MOORARI ENHANCED OIL RECOVERY PROJECTS—‘GETTING MORE OIL OUT FROM DOWN UNDER’

1986 ◽  
Vol 26 (1) ◽  
pp. 389
Author(s):  
D.J. Brown ◽  
M.R. Barley
Keyword(s):  
Oil Recovery ◽  
Rock Permeability ◽  
Full Development ◽  
Stock Tank ◽  
Productivity Decline ◽  
Infill Drilling ◽  
Gas Flooding ◽  
Incremental Oil Recovery ◽  
Cooper Basin ◽  
Early Injection

The Tirrawarra and Moorari oilfields are located in the South Australian portion of the Cooper Basin. Production to date in the fields has been maintained primarily by infill drilling, with individual wells showing substantial declines in productivity. In the absence of an active aquifer, some form of pressure maintenance was required to arrest this productivity decline and increase recovery. The miscibility characteristics of the reservoir oil, the low rock permeability, and the availability of miscible injection gases made miscible gas flooding the preferred improved recovery process.In 1984 pilot gas injection floods were initiated in both the Tirrawarra and Moorari fields to test the effectiveness of pressure maintenance and the miscible process in the Tirrawarra Sandstone. Injection was initiated using the Tirrawarra Field non-associated Patchawarra Formation separator gas to defer the high facility costs associated with alternative injection gases.The results of the pilot injection programs have been most promising. Production has improved in most offset producing wells with no sign of early injection gas breakthrough. In 1986, expansion of the Tirrawarra pilot will complete development of a major portion of the Tirrawarra Field on inverted seven spot injection patterns. Ultimate development of the field for full scale injection may involve infill drilling and well conversions resulting in skewed five spot patterns. Three alternative injection gases are being considered: Patchawarra separator gas, carbon dioxide, and ethane, each of which show favourable miscibility characteristics with the Tirrawarra oil. The incremental oil recovery expected from the full development is 23 million stock tank barrels oil.

Multi-Layer Network Performance and Reliability Analysis

2011 ◽  
pp. 114-140
Author(s):  
Kostas N. Oikonomou ◽  
Rakesh K. Sinha ◽  
Robert D. Doverspike
Keyword(s):  
Network Performance ◽  
Failure Modes ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Upper And Lower Bounds ◽  
Analysis Tool ◽  
Network Graph ◽  
Design Alternatives ◽  
Network Failures ◽  
Network States

The authors describe a methodology for evaluating the performability (combined performance and reliability) of large communications networks. Networks are represented by a 4-level hierarchical model, consisting of traffic matrix, network graph, “components” representing failure modes, and reliability information. Network states are assignments of modes to the network components, which usually represent network elements and their key modules, although they can be more abstract. The components can be binary or multi-modal, and each of their failure modes may change a set of attributes of the graph (e.g. the capacity or cost of a link). Their methodology also captures the effect of automatic restoration against network failures by including two common rerouting methods. To compute network performability measures, including upper and lower bounds on their cumulative distribution functions, we augment existing probabilistic state-space generation algorithms with our new “hybrid” algorithm. To characterize the network failures of highest impact, we compute the Pareto boundaries of the network’s risk space. The authors have developed a network analysis tool called nperf that embodies this methodology. To illustrate the methodology and the practicality of the tool, they describe a performability analysis of three design alternatives for a large commercial IP backbone network. [Article copies are available for purchase from InfoSci-on-Demand.com]

Comparative Analysis of Tracers Against Pressure Pulse Code Interference Testing based on the Numerical Simulations of the Synthetic Oilfields with Complicated Geology

2021 ◽  
Author(s):  
Artur Mikhailovich Aslanyan ◽  
Rushana Rinatovna Farakhova ◽  
Danila Nikolayevich Gulyaev ◽  
Ramil Anvarovich Mingaraev ◽  
Ruslan Ildarovich Khafizov
Keyword(s):  
Numerical Simulations ◽  
Pressure Pulse ◽  
Accurate Estimation ◽  
Reservoir Properties ◽  
Advantages And Disadvantages ◽  
Well Spacing ◽  
Reservoir Permeability ◽  
The Cross ◽  
Specific Implementation ◽  
Synthetic Models

Abstract The main objective of the study is to compare the results of the cross-well tracers survey against the pulse code pressure interference testing (PCT) for the complicated geological structures. The study was based on the numerical simulations on the synthetic 3D models with popular geological complications, such as faults, vertical and horizontal reservoir anisotropy and pinch-outs. The study has set a special focus on quantitative analysis of the reservoir properties estimated by tracers and PCT as against the known values. This provides a text-book examples of advantages and disadvantages of both surveillance methods in different geological environment. Pulse code testing is specific implementation of pressure interference testing by creating a series of injection/production rate changes accordingly to a preset schedule to create a "pressure code" and monitoring the pressure response in the offset wells. The use of high-resolution quarts gauges is highly beneficial in case of large cross-well intervals scanning or poor reservoir quality in case of regular inter-well spacing. The tracer survey is based on injecting a liquid with chemical markers and subsequent capturing the markers at surface samples in the offset wells. The modern markers are relatively cheap and can be captured at very low concentrations thus making the cross-well scanning available even for high inter-well spacing. For synthetic models with vertical inhomogeneity the PCT provides a close estimate for compound dynamic reservoir properties (transmissibility and pressure diffusivity). For synthetic models with lateral inhomogeneity the PCT provides an accurate estimation for effective reservoir thickness and permeability. Tracers survey is not able to assess the reservoir thickness. The popular methods to assess reservoir permeability from tracers survey show a substantial deviation from the true reservoir permeability for synthetic models with vertical and lateral heterogeneity. This leads to conclusion that the most reliable application of racers survey is a qualitative assessment of cross-well connectivity and quantitative estimate of permeability in homogenous reservoirs. The first study of quantitative comparison of tracer survey against pressure pulse-code interference survey. Tracer survey and PCT efficiency was compared on 3D numerical models. Presence of synthetic models, describing geological complications, which may be seen very often on real reservoirs, provides a reliable basis for comparison.

scholarly journals Measurement and simulation study on effective drainage radius of borehole along coal seam

2018 ◽  
Vol 37 (6) ◽  
pp. 1657-1679 ◽  
Author(s):  
Wei Qin ◽  
Jialin Xu ◽  
Guozhong Hu ◽  
Jie Gao ◽  
Guang Xu
Keyword(s):  
Coal Seam ◽  
Simulation Study ◽  
Drainage Radius ◽  
Effective Drainage

scholarly journals IDENTIFICATION OF INHOMOGENEOUS LAYERS AND RESERVOIR LENSES IN TERRIGENOUS DEPOSITS

Neft i gaz ◽  
2020 ◽  
Vol 1 (121) ◽  
pp. 25-39
Author(s):  
S.P. NOVIKOVA ◽  
◽  
S.V. SIDOROV ◽  
Z.M. RIZVANOVA ◽  
I.Z. FARXUTDINIV ◽  
...  
Keyword(s):  
Structural Factor ◽  
Upper Devonian ◽  
Residual Oil ◽  
Reservoir Properties ◽  
Systematic Analysis ◽  
Oil Saturation ◽  
Oil Reserves ◽  
Frasnian Stage ◽  
Net To Gross ◽  
Bypassed Oil

The possibilities of localizing residual oil reserves in depleted deposits of Tatarstan oilfield analyze in the article. The object of the study is the Pashian deposits of the Frasnian stage, Upper Devonian of the Almetyevskaya area, Romashkino oilfield. Possibilities of bypassed oil searching are considered. Sedimentation and heterogeneity of strata in the terrigenous Devonian sediments within the study area are analyzed in the paper. The analysis was carried out on the basis of well logging data. A detailed correlation of strata has been carried out, and members have been identified according to the rhythm of the layers’ occurrence in the Pashian horizon. The distribution of reservoirs and seals was studied based on the results of the detailed correlation. The analysis is based on a systematic analysis of the research object, which made it possible to consider the problem from different sides. The analysis of structural surfaces, maps of total and oil-saturated thicknesses, porosity, oil saturation, net-to-gross content and dissection along the upper and lower Pashian deposits was carried out. The influence of the structural factor on the reservoir properties of the formations is analyzed. The analysis made it possible to assess the heterogeneity of reservoirs in area and section and to predict the bypassed oil reserves localization.

MORE WELLS, MORE OIL—A CASE STUDY OF RESERVES GROWTH IN THE KENMORE FIELD

2006 ◽  
Vol 46 (1) ◽  
pp. 35
Author(s):  
J.E. Skinner ◽  
M.J. Altmann ◽  
T.H. Wadham
Keyword(s):  
Economic Value ◽  
Oil Field ◽  
Oil Fields ◽  
Seismic Survey ◽  
Current Estimate ◽  
Oil Reserves ◽  
Recoverable Reserves ◽  
Stock Tank ◽  
Infill Drilling

The Kenmore oil field in the Eromanga Basin of southwest Queensland was discovered in 1985. Since then, a further 32 wells have been drilled and more than 12.5 MMSTB of oil has been produced from the Birkhead Formation/Hutton Sandstone. Oil production over the last year has averaged 1,220 barrels per day totalling some 0.45 million stock tank barrels (MMSTB)Oil reserves in Kenmore were originally estimated at 2.2 MMSTB following the Kenmore–1 discovery well drilled in 1985. In the following 20 years, infill drilling, a 3D seismic survey, various reservoir studies and better -than-expected recovery efficiency, have steadily increased the ultimate recoverable reserves to the current estimate of 14.3 MMSTB.The growth of reserves at Kenmore is primarily attributed to better drainage of the complex reservoir framework within the lower Birkhead Formation resulting from recognition of the variable lateral connectivity of the reservoir. Due to the initial estimate of the ultimate field reserves being significantly smaller than now recognised and the resultant conservative drilling program, the economic value of the field was not maximised. This experience has implications for the ongoing development of the Kenmore field and suggests that other Birkhead/Hutton oil fields should be developed more aggressively to prevent history repeating itself.

Recovery Factor Improvement; A Success Story of Improving 10% of RF in Greater Natih Reservoirs, North of Oman

2021 ◽  
Author(s):  
Hilal Sheibani ◽  
Ratih Wulandari ◽  
Roeland van Gilst ◽  
Hawraa Al Lawati ◽  
Al Mutasem Abri ◽  
...  
Keyword(s):  
Production Efficiency ◽  
Production Optimization ◽  
Recovery Factor ◽  
Opportunity Identification ◽  
Reservoir Properties ◽  
Hydrocarbon Production ◽  
The Road ◽  
Road Map ◽  
Infill Drilling ◽  
Realization Process

Abstract Recovery Factor Improvement (RFI) is a process to check the hydrocarbon production efficiency by incorporating the actual static and dynamic field data, as well as the way how the field being operated. This has been a common process within Shell's portfolio since 2018 (Ref; Muggeridge et al., 2013 & Smalley et al., 2009). The approach has been developed to stimulate the identification of new opportunities to increase the recovery from the existing fields and to aid the maturation of these opportunities into the Opportunity Realization Process. There are four (4) factors that affected overall reservoir recovery factor, they are: Pressure efficiency; related to which pressure can be reduced in the reservoir as dictated by the relevant facilities and wells.Drainage Efficiency; the proportion of the in-place hydrocarbon that is pressure-connected directly to at least one producing well on a production timescale.The "secondary pay" efficiency; takes into account the volumes of poorer quality rock in which the gas remains at pressure above the lowest pressure just outside the wellbore (Pf) when the reservoir is abandoned.Cut-off Efficiency; the proportion of hydrocarbon that is lost due to non-production of the tail.This approach was applied in the dry gas Natih Reservoir fields in the PDO concession area. Before the implementation of RFI, the average recovery factor for Natih was around 70%. This was considered low for a homogenous-dry gas reservoir. The targeted Natih fields were benchmarked against each other with a total of 11 fields with similar reservoir properties. Post the benchmarking exercise, the expected field recovery factor is approximately ~90-93%. The team managed to map out the opportunities to achieve the targeted RF and identified the road map activities. The activities are mainly related to: production optimization: retubing, re-stimulation reduce drainage: infill drilling, horizontal well reduce the field intake through compression The outcome of the mapping was then further analyzed through integrated framework to be matured as a firm-project. The new proposed activities are expected to add around 9% additional recovery to the existing fields. There will be a remaining activities which will be studied in the future, example infill wells and intelligent completions. These will close the gap to TQ and add other addition RF of 11-13%. As conclusion, the RFI was seen as a structured approach to better understanding the field recovery factor based on the integrated surface and subsurface data with a robust analysis to trigger opportunity identification linked to RFI elements. It is similar concept as sweating the asset by generating limit diagram for each recovery mechanism & the road map to achieve the maximum limit. This paper will highlight the Natih Fields RFI analysis, highlighting the key learning and challenges.

scholarly journals A method for analyzing temporal patterns of variability of a time series from Poincaré plots

2012 ◽  
Vol 113 (2) ◽  
pp. 297-306 ◽  
Author(s):  
Mikkel Fishman ◽  
Frank J. Jacono ◽  
Soojin Park ◽  
Reza Jamasebi ◽  
Anurak Thungtong ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Distribution ◽  
Cumulative Distribution ◽  
Respiratory Pattern ◽  
Multiple Time ◽  
Analysis Tool ◽  
Process Data ◽  
Poincaré Plot ◽  
Data Set ◽  
Distribution Of Points

The Poincaré plot is a popular two-dimensional, time series analysis tool because of its intuitive display of dynamic system behavior. Poincaré plots have been used to visualize heart rate and respiratory pattern variabilities. However, conventional quantitative analysis relies primarily on statistical measurements of the cumulative distribution of points, making it difficult to interpret irregular or complex plots. Moreover, the plots are constructed to reflect highly correlated regions of the time series, reducing the amount of nonlinear information that is presented and thereby hiding potentially relevant features. We propose temporal Poincaré variability (TPV), a novel analysis methodology that uses standard techniques to quantify the temporal distribution of points and to detect nonlinear sources responsible for physiological variability. In addition, the analysis is applied across multiple time delays, yielding a richer insight into system dynamics than the traditional circle return plot. The method is applied to data sets of R-R intervals and to synthetic point process data extracted from the Lorenz time series. The results demonstrate that TPV complements the traditional analysis and can be applied more generally, including Poincaré plots with multiple clusters, and more consistently than the conventional measures and can address questions regarding potential structure underlying the variability of a data set.

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