Guo, Qingbin (PetroChina Tarim Oilfield Company) | Qiu, Bin (PetroChina Tarim Oilfield Company) | Zhao, Yuanliang (PetroChina Tarim Oilfield Company) | Fan, Zhaoya (Schlumberger) | Chen, Jichao (Schlumberger) | Han, Yifu (Schlumberger) | Zhang, Tao (Schlumberger) | Li, Kaixuan (Schlumberger) | Yu, Hua (Schlumberger) | Jiang, Lei (Schlumberger) | Wei, Guo (Schlumberger) | Yu, Daiguo (Schlumberger)
The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical.
More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis.
These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.
Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
Moreno Ortiz, Jaime Eduardo (Schlumberger) | Klemin, Denis (Schlumberger) | Savelyev, Oleg (Gazprom Neft Middle East B.V.) | Gossuin, Jean (Schlumberger) | Melnikov, Sergey (Gazpromneft STC) | Serebryanskaya, Assel (Gazprom Neft Middle East B.V.) | Liu, Yunlong (Schlumberger) | Gurpinar, Omer (Schlumberger) | Salazar, Melvin (Schlumberger) | Gheneim Herrera, Thaer (Schlumberger)
Use of numerical models to characterize and evaluate reservoir potential is an industry wide practice, with increasingly more development decisions being substantiated by finite difference models. Advances on hardware and software, along with the ability to effectively incorporate accurate process physics, makes simulation a robust tool for field development decisions, particularly on complex operations such as enhanced oil recovery and/or reservoirs with challenging heterogeneity and pore structures. Use of these models does not come without its challenges where data requirements (and use of special characterization both at lab and field level) increase as does the reservoir characterization granularity and thus model sizes. Unsurprisingly the increase of model precision and data requirements amplifies non-uniqueness of the numerical solutions obtained during any field evaluation including field development planning (FDP). Incomplete/inconsistent datasets pose a further challenge to the accuracy (and arguably risk) of the forecasts by introducing further uncertainty on the process characterization. Use of complementary technology such as digital rock, that would enable mitigate impact of such uncertainties in a timely manner -either at field or laboratory level, is thus highly desirable particularly when dealing with enhanced oil recovery. Compounding the non-linearity effect of the EOR agent characterization is the effect of the augmented numerical artifacts (dispersion, dilution, etc) of which complex chemical implementations are prone to, making the upscaling process from laboratory dimensions to field more complex.
Dommisse, Robin (University of Texas) | Janson, Xavier (University of Texas) | Male, Frank (University of Texas) | Price, Buddy (The University of Texas at Austin) | Payne, Simon (Ikon Science) | Lewis, Andrew (Fairfield Geotechnologies)
Modern reservoir characterization approaches can be greatly aided by incorporating all available data and interpretations in a three dimensional geomodel. Our goal is to offer a regional perspective to augment the interpretations from local, field-scale 3D models developed by the industry. In this work we highlight the benefits of continuous development of the geomodel for the characterization of the facies architecture of an unconventional play.We generated a three dimensional, faulted Delaware Basin geomodel, containing over 1 billion cells, including stratigraphic, petrophysical, core description, and production data for the Bone Spring and Wolfcamp intervals. The model is based on over 7,000 correlated wells, 650 wells with facies interpretations and approximately 9,000 horizontal production wells with analyzed decline curves and completion data. Additionally, a high-quality 3D seismic volume in the northeastern part of the Delaware Basin reveals the complex stratigraphic architecture of key producing intervals in the Permian Basin. The 3D volume, combined with regional 2D seismic lines, enabled refining the interpretation of the stratigraphic architecture of the Wolfcampian to Guadalupian shelf margin. This allows us to relate the slope to basin strata imaged in the 3D seismic to the well-established stratigraphic architecture of the surrounding platforms. The 3D seismic volume reveals the seismic geomorphology of several key intervals. There are two areas of focus: 1) Testing of the facies model derived from log and core analyses using different deterministic and stochastic attribute distribution techniques; and 2) Exploring the influence of geological trends on productivity. This work demonstrates the value of a multiscale, regional perspective to the practice of 3D reservoir characterization in the Delaware Basin.
This case study helped an operator in the Powder River Basin approach an optimized completion design. The operator used geomechanical measurements, hydraulic fracture modeling, and fracture diagnostics on two horizontal wells. The two wells are near a previously-completed, producing well (i.e., “parent” well).
While drilling the two horizontal wells, the operator acquired geomechanics data. This method, called drill bit geomechanics, measured the variability along the laterals. These data produced geomechanically-informed perforation and stage placements to minimize the differences in minimum horizontal stress across each stage. Additionally, the operator engineered the perforation sizes, which increased perforation friction to overcome the measured variability. The authors used the near-wellbore geomechanics data, along with other data, in a hydraulic fracture simulator. In general, standard hydraulic fracture simulators assume constant mechanical properties in each geologic layer. Compared to this standard practice, adding measured geomechanics data can more accurately predict which perforation clusters may be stimulated. To test two different fluid systems, the operator designed a “hybrid” (i.e., combination of slickwater and crosslinked gel) treatment for Well 1 and a slickwater treatment for Well 2. Fracture diagnostics reported their effectiveness. Diagnostics included: 1) proppant tracers to evaluate the perforation efficiency, 2) oil-soluble fluid tracers to quantify by-stage production contribution, and 3) water-soluble fluid tracers to assess inter-well communication. Also, the operator had used proppant tracers on the parent well, providing a baseline for results comparison.
Compared to the parent well, the two study wells showed 15-22% higher perforation efficiency. This suggests the engineered design changes created more even proppant distributions. Understanding the geomechanical variability, the operator recognized the engineering required to overcome it. The oil-soluble tracer, although affected by the parent well's depletion profile, showed higher perforation efficiency can increase oil production. Between the two study wells, Well 1 had higher perforation efficiency than Well 2 and it slightly out-produced Well 2. This suggested the hybrid design was likely the more effective design. The hydraulic fracture simulator with near-wellbore geomechanics data predicted perforation efficiency similar to that measured by the proppant tracer. Across both wells’ traced stages, the predicted efficiency and measured efficiency were within 3%. The measurements validated the modeling method.
This paper describes a method of improving completion designs through 1) geomechanics data measured while drilling, 2) modeled perforation cluster efficiency, 3) a measurement of proppant placement effectiveness, and 4) an estimate of stage-by-stage production. For the Powder River Basin operator, this method informed decisions about the next completion design iterations. Operators in any unconventional basin could apply this workflow to approach an optimized completion.
Flotron, Alyssa (Kansas Interdisciplinary Carbonates Consortium [KICC], University of Kansas) | Franseen, Evan (Kansas Interdisciplinary Carbonates Consortium [KICC], University of Kansas) | Goldstein, Robert (Kansas Interdisciplinary Carbonates Consortium [KICC], University of Kansas)
Controls on deposition and reservoir quality of mixed unconventional carbonates and siliciclastics are not adequately understood. This project explores the Wolfcamp ‘A’ (early Leonardian) in Howard County, TX to determine what stratigraphic and sedimentologic controls lead to areas with the best reservoirs.
Core, thin section, XRD, TRA, and rock-eval pyrolysis data were used to analyze sedimentary facies and rock properties. Core observations were used to calibrate facies to 1122 well logs, which were used to correlate the Wolfcamp A internal stratigraphy across an area of 3637 km2. Facies distribution and thickness were mapped in each stratigraphic package to analyze controls on distribution of high and low reservoir-quality sediment gravity flow (SGF) facies.
Out of 11 lithofacies, the dominant facies assemblages are coarse-grained packstone-floatstone-rudstone (CGC), fine-grained calcareous mudstone-wackestone (FGMW), and siliceous mudstone-siltstone (SMS). CGC facies have sharp, locally erosive surfaces, rip-up clasts, are massive or have internal grading, suggesting deposition from SGFs. The dominance of detrital quartz, lack of radiolarians, rarity of shallow-water skeletal fragments, and massive or normal graded laminations suggest SMS and FGMW were deposited as SGFs with a separate siliciclastic (SMS) or carbonate slope (FGMW) source. SMS facies have the best unconventional reservoir potential, with total porosity ranging from ~6-10%, TOC of 2-3.2 wt%, and low clay content (<50%).
Six regionally identifiable major units show progradational and compensational geometries, and each pair of major units has wedge-on-wedge relationships. The lowest two major units are CGC-rich, the middle two are characterized by SMS facies, and the upper two contain all three facies assemblages. The three major units that are thickest proximally are lobe-shaped and sourced from between the Eastern Shelf and Glasscock “nose” with internal units downlapping basinwards. The other three major units are thickest distally and laterally with internal units onlapping proximally and, with some exceptions, are mostly sourced from the Eastern Shelf and Glasscock “nose.”
The results suggest CGC facies were commonly deposited during high relative sea level whereas most SMS and FGMW facies were deposited during low relative sea level. Notch-like features in the slope acted as foci for SGFs. Promising sweet spots of greatest thickness and SMS prevalence are in the middle pair of major units and located near a northeast-southwest trend of thick SMS deposition situated medially. The distribution of those sweet spots is predictable by mapping paleotopographic funneling mechanisms, and understanding how relative sea level controls facies distribution and how paleotopography controls sediment dispersal and geometries. These controls are broadly applicable across areas of the Permian Basin.
Seismic attributes can be both powerful and challenging to incorporate into interpretation and analysis. Recent developments with machine learning have added new capabilities to multi-attribute seismic analysis. In 2018, Geophysical Insights conducted a proof of concept on 100 square miles of multi-client 3D data jointly owned by Geophysical Pursuit, Inc. (GPI) and Fairfield Geotechnologies (FFG) in the Denver-Julesburg Basin (DJ). The purpose of the study was to evaluate the effectiveness of a machine learning workflow to improve resolution within the reservoir intervals of the Niobrara and Codell formations, the primary targets for development in this portion of the basin.
The seismic data are from Phase 5 of the GPI/Fairfield Niobrara program in northern Colorado. A preliminary workflow which included synthetics, horizon picking and correlation of 28 wells was completed. The seismic volume was re-sampled from 2 ms to 1 ms. Detailed well time-depth charts were created for the Top Niobrara, Niobrara A, B and C benches, Fort Hays and Codell intervals. The interpretations, along with the seismic volume, were loaded into the Paradise® machine learning application, and two suites of attributes were generated, instantaneous and geometric. The first step in the machine learning workflow is Principal Component Analysis (PCA). PCA is a method of identifying attributes that have the greatest contribution to the data and that quantifies the relative contribution of each. PCA aids in the selection of which attributes are appropriate to use in a Self-Organizing Map (SOM). In this case, 15 instantaneous attribute volumes, plus the parent amplitude volume, were used in the PCA and eight were selected to use in SOMs. The SOM is a neural network-based machine learning process that is applied to multiple attribute volumes simultaneously. The SOM produces a non-linear classification of the data in a designated time or depth window.
For this study, a 60-ms interval that encompasses the Niobrara and Codell formations was evaluated using several SOM topologies. One of the main drilling targets, the B chalk, is approximately 30 feet thick; making horizontal well planning and execution a challenge for operators. An 8 X 8 SOM applied to 1 ms seismic data improves the stratigraphic resolution of the B bench. The neuron classification also images small but significant structural variations within the chalk bench. These variations correlate visually with the geometric curvature attributes. This improved resolution allows for precise well planning for horizontals within the bench. The 25 foot thick C bench and the 17 to 25 foot thick Codell are also seismically resolved via SOM analysis. Petrophysical analyses from wireline logs run in seven wells within the survey by Digital Formation; together with additional results from SOMs show the capability to differentiate a high TOC upper unit within the A marl which presents an additional exploration target. Utilizing 2d color maps and geobodies extracted from the SOMs combined with petrophysical results allows calculation of reserves for the individual reservoir units as well as the recently identified high TOC target within the A marl.
The results show that a multi-attribute machine learning workflow improves the seismic resolution within the Niobrara reservoirs of the DJ Basin and results can be utilized in both exploration and development.
The mainly Cenomanian Shilaif formation of Abu Dhabi (UAE) is currently explored and appraised for its shale oil and shale gas potential. The objective is to assess the hydrocarbons resources, the spatial variability of rock and fluid properties as well as highlighting sweet-spots.
The exploration efforts started in 2014, conducting some multidisciplinary regional depositional and petroleum system studies complemented with exploration wells and the acquisition of comprehensive suites of logs, cores and pressured (sidewall) cores.
The Shilaif formation was deposited in a deeper water intrashelf basin and is time equivalent to the adjacent shallow water higher energy Mishrif formation. Non-eroded Shilaif thicknesses vary from 500 to 900 ft from deep basin to slope respectively. The formation can be subdivided into 3-4 composite sequences each with separate source rocks and clean tight carbonates.
The present day structural configuration is inherited from two related regional compressional events; a) a NW-SE compression responsible for the anticline/syncline, lasting from Late Cenomanian to Early Eocene was created by India's continental drift, b) the late Cretaceous (starting in Turonian) emplacement of the Semail ophiolite from NE direction responsible for loading the continental plate and resulting in the creation of a large scale foreland basin. Reactivation of this NE compression occurred during Late Tertiary.
The resulting structuration created two synclines in the south of Abu Dhabi with maximum maturity of 1.1 Vr (TR 0.65). The foreland basin towards the North East has maturity values reaching the dry gas window. The continuous present day stress from a NE direction combined with high overpressures has a strong geomechanical impact with hmin close to overburden in synclinal areas.
This study aims to present the unconventional resource potential of the Late Albian to Early Turonian basinal sequences in Abu Dhabi.
Insights into the local and regional stratigraphic framework as well as structural controls of the depot-centers are presented.
Evans, Kaitlin (West Virginia University) | Toth, Randy (West Virginia University) | Ore, Tobi (West Virginia University) | Smith, Jarrett (West Virginia University) | Bannikova, Natalia (West Virginia University) | Carr, Tim (West Virginia University) | Ghahfarokhi, Payam (West Virginia University)
Data obtained from the Marcellus Shale Energy and Environment Laboratory (MSEEL) project was used to understand how pre-existing fractures behave under elevated pore pressure. 1680 pre-existing fractures were identified along the lateral of the MIP-3H well. Image logs and 3D computer tomography (CT) scan of the cores was used for fracture location and most fractures were identified as calcite-filled and resistive. In addition, sonic scanner well logs provided minimum horizontal stresses for every few feet, and the pilot-hole density log provided the vertical stress at each point along the lateral. This collection of geologic and geomechanical data helped us to establish an anisotropic stress field with separate stress tensors for each stage. Twenty-eight stress tensors were constructed corresponding to twenty-eight completion stages within the MIP-3H well. The vertical stress component of the tensors was calculated by integrating MIP-3 pilot-hole density log to the average depth of each stage. The minimum horizontal stresses (Shmin) were also calculated by averaging the recorded Shmin readings in each stage. Maximum horizontal stress (Shmax) was calculated by a third-party logging vendor by adding a 400 psi to the Shmin values. The stress tensors were transformed into a geographic coordinate system along with the dip and strike of each fracture. The transformed coordinate system (North-East-Down) was used when applying Cauchy's Stress theorem to every singular fracture within each stage to calculate the normal and shear stress components on each fracture. A Mohr diagram was created for each stage with two failure criteria lines corresponding to mu (μ) values of 0.6 and 1.0. Fractures are displayed on the diagram using their calculated normal and shear stresses. The pore pressure increase found from the average treatment pressure for each stage was applied and whether natural fractures experienced tensile or shear failure was inspected. The objective is to understand if natural fractures experience shear failure or tensile failure during hydraulic fracturing and determine if there is a contrast in the response between resistive (calcite-filled) and conductive fractures. It was observed that prior to hydraulic fracturing, resistive natural fractures are mechanically dead and are in the stable region of the Mohr diagrams. Results show that although the majority of the pre-existing fractures are identified as resistive and mineral-filled, they undergo tensile failure when pore pressure was increased during hydraulic fracturing.
Zhang, Hui (PetroChina) | Wang, Lizhi (Schlumberger) | Wang, Zhimin (PetroChina) | Pan, Yuanwei (Schlumberger) | Wang, Haiying (PetroChina) | Qiu, Kaibin (Schlumberger) | Liu, Xinyu (PetroChina) | Yang, Pin (Schlumberger)
Located at the foothills of Tianshan mountains, western China, the Dibei tight gas reservoir has become one of the key exploration areas in last decade because of its large gas reserve potential. The previous exploration effort yielded mixed results with large variations of the production rates from these exploration wells and many rates are too low to be deemed as discovery wells. Petrophysical properties were excluded as controlling factors because these properties for most exploration wells are very similar. Under the large tectonic stress, heterogeneous natural fracture systems are induced and unevenly distributed in the reservoir, which might be the controlling factor for production. However, due to the limitation of the seismic data quality, quantitative fracture modeling with seismic is not possible for this field. A new method predicting the 3D occurrence of the natural fractures in the reservoir is needed.
In this study, geomechanics-based methods were used to predict the natural fracture systems in the reservoir. The methods started from classification of natural fracture systems based on borehole image and core data into either fold-related and/or fault-related fractures. Geomechanics-based structure restoration was conducted to compute the deformation and the perturbed stress field from the restoration of complex geological structures through time. A correlation was established between the fold-related perturbated stress field and the occurrence of fold-related fractures from wells to predict the 3D occurrence of this type of natural fractures. Meanwhile, the computation of the perturbed stress field around 3D discontinuities (i.e. faults) for one or more tectonic events was conducted by the Boundary Element Method (BEM) until a good match was achieved between the fault-related perturbed stresses and observed fault-related fractures from the wellbore. By using the output from the two methods, the discrete fracture network (DFN) model was constructed to explicitly represent the occurrence and geometry of the natural fracture system in the reservoir in a geological model. A geomechanical model was constructed based on an integrated workflow from 1D to 3D. The fracture stability was then calculated based on the 3D geomechnical model.
Detailed analysis was conducted among the DFN model, the geological model of the reservoir and productivity of the exploration wells, and very good correlation was revealed between the productivity of the exploration wells and the occurrence and geometry of the natural fractures and the structural position of the reservoir.
This study shows that geomechanics-based methods efficiently capture the occurrence of natural fracture systems and reveal the production-controlling factors of the tight gas reservoir. It demonstrates that geomechanics is a powerful tool to support successful exploration of the tight gas reservoir in tectonically stressed environments.