bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Study of Shear Wave and Resistivity Anisotropy in an Inclined Well Providing Insight to the Geomechanical Model
Authors: Das Saikat , Zhang Tingting , Vevakanandan Valsan , Abd Rahim Mohd Helmi
Published: April 2019

Understanding in-situ stress orientations and magnitudes is critical in building a geomechanical model which helps in planning and execution of a development and production programme for any hydrocarbon reservoirs. The azimuthal anisotropy analysis from cross-dipole acoustic data is commonly used to derive the direction of maximum-horizontal stress. However, the interpretation of the stress orientation is challenging in inclined wells where anisotropy may also be influenced by the relative angle of bedding plane to the bore hole. Integration of the data becomes of paramount importance to correctly interpret the stress distribution.

Cross dipole wireline acoustic, 3D resistivity and 6 arm calliper data were acquired in a deviated well, offshore, Malaysia. Acoustic data was processed for azimuthal anisotropy, 3D-resistivity data was processed for formation dip, azimuth, horizontal and vertical resistivity and 6 arm calliper data was used to generate borehole shape. Acoustic analysis provided the difference in fast and slow shear wave velocities and the azimuth of fast shear. The resistivity anisotropy, dip and azimuth and bore hole shape information was incorporated to interpret effect of the dipping bed in the scheme of relating acoustic anisotropy to the formation stress.

Meaningful difference in the fast and slow shear velocities (in two orthogonal direction) is observed in this well. The fast shear wave azimuth of NW-SE is consistent with the regional trend. However, the presence of laminated shale interval in the inclined bore hole imparts uncertainty in relating the anisotropy to the stress field. The formation dip and azimuth obtained from the resistivity anisotropy provided the framework of the interpretation by identifying the intervals with higher relative dip and the associated anisotropy perceived by it. Bore hole ovalization also provides the necessary input to the interpretation scheme which is supported by the existing field wide geomechanical model.

Integrating all datasets resolved potentially ambiguous interpretation of the source of azimuthal acoustic anisotropy. This approach determines the cause of the anisotropy (unbalanced stress in formation vs. dipping beds and shale transverse anisotropy). The result provides valuable information to refine the existing geomechanical model which can be used in future well placement and planning, optimum mud weight design, and constraining water injection operating limit during the life of the field.
Improved Estimation and Forecast Through Model Error Estimation – Norne Field Example
Authors: Lu Minjie , Chen Yan
Published: April 2019

The ensemble based methods (especially various forms of iterative ensemble smoothers) have been proven to be effective in calibrating multiple reservoir models, so that they are consistent with historical production data. However, due to the complex nature of hydrocarbon reservoirs, the model calibration is never perfect, it is always a simplified version of reality with coarse representation and unmodeled physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when ‘perfect’ model parameters are used as model input is known as ‘model error’. Assimilation of data without accounting for this model error can result in incorrect adjustment to model parameters, underestimation of prediction uncertainties and bias in forecasts.

In this paper, we investigate the benefit of recognising and accounting for model error when an iterative ensemble smoother is used to assimilate production data. The correlated ‘total error’ (combination of model error and observation error) are estimated from the data residual after a standard history matching using Levenberg-Marquardt form of iterative ensemble smoother (LM-EnRML). This total error is then used in further data assimilations to improve the model prediction and uncertain quantification from the final updated model ensemble. We first illustrate the method using a synthetic 2D five spot case, where some model errors are deliberately introduced, and the results are closely examined against the known ‘true’ model. Then the Norne field case is used to further evaluate the method.

The Norne model has previously been history matched using the LM-EnRML (Chen and Oliver, 2014), where cell-by-cell properties (permeability, porosity, net-to-gross, vertical transmissibility) and parameters related to fault transmissibility, depths of water-oil contacts and relative permeability function are adjusted to honour historical data. In this previous study, the authors highlighted the importance of including large amounts of model parameters, proper use of localization, and adjustment of data noise to account for modelling error. In the current study, we further improve the aspect regarding the quantification of model error. The results showed promising benefit of a systematic procedure of model diagnostics, model improvement and model error quantification during data assimilations.
Combining Dimensional Analysis and Neural Networks to Improve Flow Assurance Simulations
Authors: Bratland Ove
Published: April 2019

When developing mathematical models for two- and three-phase flow in long pipelines, the most difficult challenge is to model the frictions, volume fractions and flow regimes accurately. This paper combines 3 different methods when confronting that problem: Dimensional analysis, mechanistic models, and Neural Networks (NN). It is shown that those methods supplement each other in important ways. The dimensional analysis is helpful in upscaling laboratory measurements to full-scale flowlines. In case of 3-phase gas oil water flow, the number of dimensionless groups turn out to be 14. NNs offer a way of correlating that many variables, and that allows the model to account for all dimensionless groups for all types of flow. That overcomes the limitations inherent in the more common practice of focusing on only a few parameters or dimensionless groups for each type of flow regime. But introducing NNs creates a new challenge: We need data to train them.

The last problem is partly dealt with by building on well-established mechanistic models with various factors inserted. It is those factors which are trained by the NNs, not the dependent dimensionless groups themselves. Using mechanistic models rather than a pure "black box" approach leads to much faster training and more accurate results. That has made it possible to train the NNs based on a more moderate and therefore realistic amount of data than would otherwise be required.

The novel approach has been used to develop new software. The FlowRegimeEngine, as it is called, is now incorporated in several steady-state and transient commercially available computer codes. At the end of this presentation results from one of them, FlowlinePro, have been compared to results from the well-established computer code OLGA. The results turned out to be very similar.

The presented dimensional analysis also provides an interesting way of testing commercial software by checking whether results are dimensionally consistent. When doing steady-state simulations with two or more different data sets, chosen so that they form the same independent non-dimensional groups, the resulting dependent dimensionless groups should come out identical. If they do not, it is reason to treat the results with suspicion. When applying the test to FlowlinePro and OLGA, they both passed it nicely for the data-sets chosen here.
Improving Drilling Simulation Computation Performance with Smart Logic and Machine Learning
Authors: Mu Chao , Liu Jitang , Chen Rongbing , Bolchover Paul , Suryadi Hendrik , Yu Tao
Published: April 2019

Modeling and simulation play a key role in well construction planning, which can help to evaluate and optimize the engineering designs for a well. Today, many simulations use finite element analysis (FEA) and computational fluid dynamics (CFD) to model complex dynamic downhole conditions and behaviors of drilling tools. However, one challenge is that the complex simulation may take a few hours to run, which limits the usage to only a few well planning jobs. This limitation also poses as barrier in real-time monitoring applications, where under one second computation speed is required. In this paper, two approaches are presented for improving the performance of drilling simulations: smart depth selection logic for BHA tendency calculation, and reduced order model using machine learning for motor optimization modeling.
An Innovative Reservoir Engineering Method to Identify Bypassed Oil and Derisk the Further Development of a Complex Mature Field in Offshore, Malaysia
Authors: Mandal Dipak , Musani Nazim , Mohmad Nis Ilyani
Published: April 2019

In today's fast paced and challenging oil industry, the need of faster evaluation studies for quick generation of field development plan (FDP) is becoming more crucial to remain competitive. Field's geological and structural complexity, uncertainty of production data adds to the challenges. Traditional approach of building dynamic mesh models carrying out numerical simulation to history match, then predict has always remained time consuming in large mature fields.

The ‘B’ field in Peninsular Malaysia is a mature clastic with stacked reservoirs having a huge gas cap with moderate aquifer. Significant production over last 30+ years led to uneven movement of the gas cap and also of the edge aquifer leading to possibility of bypassed oil. The updated dynamic model could not match the preferential gas cap movement, thus failed to match the high GOR of downdip wells and also unable to match high watercut of certain updip wells. To identify the areas of bypassed oil thus is a significant challenge with the current dynamic model. New engineering tools of polygon balancing, material balance, normalized EUR bubbles were used with the 3D static model volume and the facies understanding. The uncertainties and risks were also identified and clear measurable methods were proposed to address the uncertainties and reduce the risks. Very detailed decision tree with clear data gathering plan to drill successive optimum wells have been planned during the campaign.

This paper details the new engineering tools used to delineate and quantify the bypassed oil in these huge clastic reservoir with preferential gas and water movement, unable to be history matched by the dynamic model. It explains the engineering methods applied to identify and quantify the 10 infill wells proposed for the development campaign. To reduce risks, this paper would also explain the blind testing that was carried out on for this new reservoir engineering analysis tool by deriving the infill potentials of the previous campaign (4 years back) by the same method.

The paper details how robust technical development plans were generated having infill well locations and reserve determination. This paper will also demonstrate the classic "Do-Learn-Adapt" strategy through its infill wells prioritization & ranking, subsurface de-risking analysis, data acquisition and mitigations plans.
Forward Integration of Dynamic Data into 3-D Static Modeling Significantly Improves Reservoir Characterization
Authors: Kayode Babatope , Meza Otto , Quintero Nerio , AlDossary Shaikha
Published: April 2019

Geo-modelling is usually done to honor static data such as core, well logs and seismic acoustic impedance (AI) map where available. Once the static geo-model is complete, history matching is carried out by tuning the static model properties until the model reproduces observed dynamic behavior. The objective of this paper is to showcase how a systematic a priori integration of dynamic elements into geo-modelling eliminated the need for history matching. These dynamic elements are; connected reservoir regions CRR (Kayode et.al 2018) and permeability-thickness (kh) interpretation from Pressure Transient Analysis PTA. This paper also introduces the concept of CRR based permeability modeling.

CRRs were defined based on time-lapse shut-in pressure trend groups. Core and log data were grouped on the basis of the identified CRR and used to build CRR-based Neural Network models for predicting permeability logs of non-cored wells within each CRR. The geo-modeler then created two geo-realizations by using the permeability logs within each CRR to distribute permeability within the CRR using two assumptions of variogram lengths (i) variogram range obtained from analysis of limited core data, (ii) variogram range required to ensure intra-CRR connectivity. Pressure transient was simulated for wells with observed PTA data using the two realizations, and a comparison of the log-log plots of simulated pressure transient derivative and observed pressure transient derivative were used to determine the quality of each realization for each well. The realization that provided the least squares of error across all the wells was selected as base-case geo-model. Permeability correction coefficients were applied on the base-case geo-model until PTA kh were acceptably matched. The resulting permeability log at the PTA well is referred to as PTA-corrected permeability log. Some cored wells were originally exempted from the neural-network permeability modelling because they didn't have logs (sonic, density and neutron logs). Hybrid permeability logs were derived from a combination of the predicted permeability logs and core permeability at these well locations.

All permeability correction logs (i) PTA-corrected permeability logs and (ii) Hybrid permeability logs were then fed back into the geo-modeling workflow to generate an improved permeability distribution which respects core data, PTA kh, and CRRs.

The do-nothing simulation run has more than 80% of wells’ pressure data acceptably history matched. This application demonstrates that a priori integration of dynamic elements like CRR, PTA kh, and the use of CCR-based permeability modeling results in a better characterized geo-model with potential for eliminating the need for history matching.
Insights into the Extra Heavy Oil Property and Oil Charging Combing Rock Pyrolysis Paramenters, Geochemistry Data and Seismic Multi-Attribute Transformation
Authors: Wang Bingjie , Xu Changgui , Wu Kui , Zhang Rucai , Deng Jun , Guo Naichuan
Published: April 2019

A new oil property identification parameter (Pw) is derived which represents the total hydrocarbon generation and pyrolysis hydrocarbon. Using continuous measurement data (Pw) and a series of sample-based attributes from 3D seismic, a strong linear trend is observed. This trend linear is used to calculate heavy oil property data in 3D volume. At the same time, we have got the relationship between reservoir physical and oil properties. Based on this, the core data and geochemical data are used to study the charging of crude oil.
Integrated Geology Sweet Spot and Microseismic Monitor to Optimise Reservoir Stimulation - A Case for Shale Gas, China
Authors: Liu Wei , He Zhenhua , Cao Junxing , Zhang Jianjun , Xu Gang , Wan Xiaoping , Yu Gang
Published: April 2019

Shale play, as one kind of non-conventional natural gas resource, has become the focus of domestic and overseas research in recent years. shale is pertained to be a reservoir with an ultralow porosity and permeability, its occurrence mode, accumulation pattern of natural gas, as well as development model are remarkably different conventional oil gas reservoirs, its development must be implemented by some special technique, e.g. horizontal drilling, drilling geologic steering, hydraulic fracturing, microseismic monitor, etc. Research shows, shale gas production depends on two factors, one is geological sweet spot factor, e.g. Total organic carbon, brittleness, core fluid pressure, micro-fractures, high quality shale thickness etc., the other is engineer technique factors, e.g. horizontal drilling, drilling geologic steering, hydraulic fracturing, microseismic monitor, fracturing schemes, etc. Single factor, sweet spots or engineering technique factor often not guarantee shale gas highly production, only when the most optimal combination of both, can achieve shale gas production maximization. How to integrate sweet spots and engineering technique to guide shale gas exploration and development? This is a serious question. This question involves to many fields, including geology and engineer sweet spots, horizontal well location deployment, drilling geologic steering, pre-fracturing warning and design, hydraulic fracturing design real-time adjustment, microseismic monitoring, etc., sweet spots results runs through the entire shale exploration and development. In this article, we will pay more attention to demonstrate that how to adjust the fracturing scheme and optimize the reservoir stimulation in real time by integrated geological sweet Spot and microseismic monitor.
Improving the Effective Supporting and Fracturing Technology is the Key to the Successful Stimulation of Low-Permeability and Low-Rank Coalbed Methane Reservoirs
Authors: Wang Xin , Zhu Qingzhong , Zheng Wei , Lu Haibing
Published: April 2019

China has abundant low-rank coalbed methane resources. The research object is the low rank lignite seams in Jiergalangtu Sag in Erlian Basin. The reservoir has low porosity and low permeability, and it has no natural productivity. The coal seams have a burial depth of 200-600 meters, thickness of 40-60 meters, and Ro of 0.32% to 0.47%. Borrowing the idea of well completion experience for the conventional low rank coal seams in the region, open hole cavity completion techniques were adopted in two wells, obtaining an output of only about 150 m3/d. The conventional active water fracturing was also tested in another well, and the output after fracturing was 200-300 m3/d. The effect of stimulation was very poor, which limited commercial exploitation activities in the region. This paper introduces two techniques to improve the effect of stimulation by improving induced fracture extension and supporting capacity in the coal seams, including the hydraulic blasting & grouting caving fracturing technique and the reverse compound fracturing technique, which were applied in two wells. A constant rate of production after fracturing reached 1,500-2,000m3/d, which was well above the lower limit output of economic exploitation in the region of 600m3/d. Exciting results were obtained. The exploration of these techniques is of great significance for low rank coalbed methane stimulation, which can help us to implement effective fracturing stimulation operation in low rank coal seams to obtain the best production effect.
Less Damaging Drilling Fluids: Development and Lab Testing
Authors: Alkhalaf Sara , Alawami Mohammed , Wagle Vikrant , Al-Yami Abdullah
Published: April 2019

The properties of the selected drilling fluid must be carefully planned to have minimal effects on the near-wellbore pore spaces. Proper mixing, monitoring, and maintenance of the drilling fluid throughout the drilling operations are as critical as the careful planning. Solids control equipment should be operated to remove the cuttings and maintain the density and rheological properties consistent.

The characteristics of an effective reservoir fluid system include stability at high pressures and temperatures, proper and stable density, good filtration control, ability to transport cuttings, and minimal damage to formation pore spaces, Davidson et al. 1997. Selection of the most suitable drilling fluid additives takes into consideration numerous factors such as downhole conditions (pressure and temperature), formation type and petro physical properties, and the objective of the drilling operation.

The experimental work in this paper involved rheological properties, thermal stability, API and HT/HP filtration and acid filter cake removal efficiency.

Tangentional flooding showed that water based Mn3O4 drill-in fluid has the highest return permeability compared to the typical drill-in fluids (KCl/CaCO3/Barite and potassium drill-in fluids). Potassium formate drill-in fluid filtrate was not compatible with brine. This incompatibility explained its low return permeability in spite of its low solids content. Oil based drilling fluid was developed and tested with good acceptable results. Filter cake removal efficiency was showing more than 95%, indicating its removable formation damage.
Molecular Simulation of Methane Adsorption Behavior in Kerogen Nanopores for Shale Gas Resource Assessment
Authors: Cao Jinrong , Liang Yunfeng , Masuda Yoshihiro , Koga Hiroaki , Tanaka Hiroyuki , Tamura Kohei , Takagi Sunao , Matsuoka Toshifumi
Published: April 2019

In this paper, we present an improved method to predict the methane adsorption isotherm for a real shale sample using molecular dynamics (MD) simulation with a realistic kerogen model. We compare our simulation results both to the experiment and to the simulation results on the basis of a simple graphite model, and show how our procedure leads to the creation of more accurate adsorption isotherms of a shale sample at a wide range of pressure. A Marcellus shale sample was chosen as an example to demonstrate how to calculate the adsorption isotherms using MD simulations. Type II kerogen molecular model was selected for the dry gas window. The constructed bulk kerogen model contains mesopores (> 2 nm) and micropores (≤ 2 nm) inside. Ten different mesopore sizes of kerogen nanopore systems were constructed. According to the characteristics of methane density distribution in the simulation system, three regions can be clearly distinguished, free gas, adsorbed gas, and absorbed gas. We show that the adsorbed gas per unit pore volume increases with the pore size decreased. This is similar to previous molecular simulations with graphite model. For predicting the total adsorption isotherm of a real shale sample, both adsorbed and absorbed gas were considered. For the adsorption amount, the calculated adsorption isotherms were averaged based on pore size distribution of that Marcellus Shale sample. For nanopores smaller than 5 nm, we used total organic carbon (TOC) data to weight the absorption contribution in the kerogen bulk (i.e. inside the micropores). The total adsorption isotherm thus obtained from our simulations reproduced experiments very well. Importantly, kerogen model has overcome the difficulties of prediction using graphite models (i.e. an underestimation of adsorption under high pressure conditions) as documented in previous studies. Furthermore, we predicted the adsorption isotherms for higher temperatures. With the temperature increased, lower adsorption amount is predicted. The novelty of our improved method is that it is able to predict methane adsorption isotherm at a wide range of pressure for a shale sample by considering both adsorption in kerogen mesopores and absorption in kerogen bulk. It can be readily used for any shale sample, where the pore size distribution, porosity, and TOC are known. We remark that the above results and conclusion resulted from our simple assumption. Further discussion might be necessary.
Reviving Idle Wells and Unlocking Potential Production Gain in Offshore Sarawak Through Exposing BCO-LRLC Opportunities
Authors: C A Razak M Amin Nizar , Kamarul Zaman M Naquib , Ali Affira , M Shah Jamari , Sakdillah M Zaki , Md Zainuri Md Zarin
Published: April 2019

Brownfield in Balingian and Baram Delta have handful of idle wells and well to be abandoned in their inventories. The project aims to reduce the idle well inventories and support production gain through monetizing behind casing opportunities. The target is to appraise and develop LRLC potentials with lower cost of appraisals. This will maximize full field potentials before abandonment and leads to future development of LRLC opportunities as conventional reservoir becomes more difficult to develop.

The idle well inventory has grew up due to problem in production (increase water cut, HGOR) and well problems (sand, fish). An order has been introduced to reduce the idle well list up to 50%. Additionally, in the past, the LRLC intervals were often ignored and considered as water-wet sands due to high water saturation or as tight sands. These intervals, that contain significant reserves, are recognized in many technical papers explaining its identification and evaluation techniques from well-data (logs and samples/cores). The scope of the project is to rejuvenate the idle wells by add-perf LRLC reservoirs.

It is impossible to achieve the target without the presence of proper and improved LRLC BCO evaluation process, thus an integrated workflow approach (between Petrophysicist, Reservoir Engineer, Production Technologist, Asset manager & Well Intervention group) has been developed and applied in the project. A new evaluation tools had also been developed called REM (Resolution Enhanced Modelling) in order to improve the log properties of LRLC reservoirs so that the data obtained from old conventional tools can still be used to evaluate LRLC reservoir. Although LRLC is termed UNSEEN, the risk is reduced by proper understanding of hydrocarbon column and sand development.

To date, 7 fields are already benefitted from this approach. Field A LRLC reservoir for example has tripled the hydrocarbon saturation, and net to gross has improved to 20% using REM compare to 5% without REM. The other 6 fields are also gaining the same increase in the properties. This has resulted in a cumulative potential of 4.4 MMstb of reserves addition and ~11 KBopd potential gain. As a result, a better and attractive BCO proposals can be generated from LRLC opportunities. The exercise will provide the company with cheaper options of appraising and developing LRLC reservoir while reducing the idle wells. There is no better way of understanding LRLC reservoir; as no tools can identify & quantify it yet, rather from the actual production.
Accelerate Non-Metallic Seal Development for Downhole Applications by Applying a Big Data Method in Material Data
Authors: Huang Xiaobin Betty , Furlan Wayne
Published: April 2019

Non-metallic materials such as plastics and elastomers are widely used for sealing in downhole operations of the oil and gas industry. These materials are used because of their inherent elasticity and resilience over a wide range of deformation, and their non-corrosive nature compared to metals. Downhole non-metallic seal development is an exceptionally complicated task with huge uncertainties due to the hostile operating environment and service longevity. In addition, the field operation is expensive; failure of the downhole seals could cost millions of dollars. Therefore, prior to the release of a non-metallic seal product for downhole applications, extensive material tests should be done in every step in its development from the material selection/development, seal design, seal manufacture to the quality control of the final product to ensure the seal could be safely used in downhole conditions for the service time.

This paper presents a big data method based on tremendous amount of material data to accelerate the downhole seal development. Traditional seal development starts with previous experience to select the seal material and relies greatly on the engineering tool test to validate and optimize the seal design. In contrast, the materials big data method can speed up the development cycle, reduce the uncertainty of the design, minimize the engineering hours spent on data search, guarantee the source and qualify of data used, eliminate redundant testing, and secure the data storage and sharing, etc. Our business experience showed that the materials big data method could reduce 50% human resource while achieving multiple times of productivity.
The Use of Drones for Innovative Seismic Acquisition: A Change of Paradigm for HSE
Authors: Masoni Isabella , Pagliccia Bruno , Thalmann Guillaume
Published: April 2019

The use of drones in the oil and gas industry is still relatively recent, and is currently unlocking new methods and approaches of geophysical acquisition and subsurface imaging. METIS®, a disruptive and integrated research project, employs the use of drones to perform an innovative 3D high density geophysical acquisition, that targets hard-to-access acreage. The benefits associated with the use of drones are easily recognized: an increased efficiency, fewer man hours, reduced HSE risks, and a lower environmental footprint. However a number of new safety, security, regulatory, and public perception issues are raised and need to be better understood before the use of drones can become standard practice. The acceptability of drones and a new method to assess the risks associated to METIS® drone operations is investigated. This study presents how the use of drones is changing the HSE risks associated with an onshore geophysical acquisition, but also how this technology brings new solutions to reduce them.
Eagle Ford Well Insights Using Data-Driven Approaches
Authors: Luo Guofan , Tian Yao , Sharma Akash , Ehlig-Economides Christine
Published: April 2019

Eagle Ford shale in South Texas is a major oil and gas production play of in the US Gulf Coast region. While some attribute the successful well performance of Eagle Ford to the technology advancement such as horizontal drilling and hydraulic fracturing, others credit the role of geological settings. However, it is still unclear what the individual or combined effects from these two sides are. Data-driven approaches, including Partial Least Square (PLS), Random Forest (RF), and Deep Neural Network (DNN), reveal relationships among the production, geological settings, and completion strategies.

In this study, we considered six-month cumulative oil production as the well performance criterion for horizontal wells completed from 2015 to 2017. We selected completion parameters such as perforation length, proppant loading, and fluid volume. We selected structural depth, lower Eagle Ford Shale thickness, total organic carbon (TOC), number of limestone beds, and average bed thickness as the key geological controls on regional production.

We calculated Spearman correlation coefficients to detect correlated input parameters and applied Singular Value Decomposition (SVD) to identify redundant input parameters. Then we performed partial linear square (PLS) regression to predict the six-month oil production from geological and completion parameters. We then used random forest (RF) and deep neural network (DNN) as non-linear machine learning techniques to predict six-month oil production and compared the prediction accuracies for these techniques against the recorded well performance using the coefficient of determination and mean squared error as criteria. Last, we ranked the relative importance of each input parameter using RF and Minimum Redundancy Maximum Relevance (MRMR).

This paper first provides the rational of input variables selection. Then the construed model helps understand the effects of completion designs and geological variables on well productivity in the Eagle Ford. This might provide valuable information to help to make decisions for new well development. This concept can be generalized among other plays.
A New Technique to Support Future Energy Exploration of Continental Sedimentary Basin: BWH Full-Frequency Fidelity and Amplitude Preserving Processing Technology
Authors: Bao Yi , Wang Cheng , Chen Shu-min , Wang Jian-min , Chen Zhi-de , Pei Jiang-yun , Wu Jia-yi
Published: April 2019

The existing seismic data in the deep layer of Songliao Basin have low vertical resolution, poor imaging accuracy and weak ability to depict anisotropy of seismic data, so the seismic data can not meet the geological requirements of fine target characterization. In order to identify thinner reservoirs and smaller faults in deep and complex structural areas, to complete sequence subdivision on volcanic rocks of Yingcheng formation and the dense sand of Shahezi formation, BWH seismic data acquisition is deployed in Anda sag. Aiming at the characteristics of clear description of BWH wave field but low SNR. A Full-frequency Fidelity and Amplitude preserving processing Technology (Flow) supported by surface consistent time-varying pulse deconvolution and viscoelastic medium prestack time migration and depth migration techniques is formed. Compared with the old data, the target band width of the BWH data has been widened by 15 Hz, the imaging quality of the complex structural area is improved obviously, and the prediction coincidence rate of the thin sand body over 8m is increased by more than 10 percentage points. The thin interbed is developed in continental sedimentary basin, and the horizontal heterogeneity is serious. The BWH acquisition and full frequency amplitude preserving processing technology will bring a new solution for fine target exploration in deep and complex structural area of Continental Sedimentary basin.
Great Breakthroughs and Key Technologies in Exploration of Ordovician Deep Buried Hill in Northern Jizhong Depression
Authors: Zhang Yiming , Tian Jianzhang , Yang Dexiang , Chen Shuguang , Liu Xing , Hou Fengxiang , Tian Ran , Zhang Chuanbao
Published: April 2019

The study area is located in the Langgu sag of Northern Jizhong depression, Bohai Bay Basin, East China. In order to achieve exploration breakthrough in deep buried hill, key engineering technologies are developed and used to accurately demonstrate important target identification by recognizing new hydrocarbon accumulation patterns resulting from the analysis of multi-stage structure-controlled trap mechanism and the detailed study of controlling factors over high-quality Ordovician reservoirs based on new high-accuracy 3D seismic data. This study reveals a new evolution mechanism of buried hill controlled by structural superposition, experiencing "the uplift from thrusting in Indo-Chinese to early Yanshan epoch, uplifted block faulting into horsts in middle Yanshan epoch, horsts tilting into belt in Eocene, and belt reversion into trap", and thus puts forward a new mechanism for reservoir forming controlled by a superposition of "dolomite, karsting, and faulting". Three types of reservoir development are identified, including "regional layered pore, local block micropore-fracture, and fracture hole pore layer-block composite", and an accumulation pattern in deep buried hill is constructed, characterized by "efficient hydrocarbon supply from gas-type source rock, predominant migration through fractured surface-nonconformity surface, and stratum- and mass-controlled accumulation", which has guided the 40 years' exploration of Ordovician Yangshuiwu buried hill zone and made a great breakthroughs. Novel relevant exploration technologies have been developed, involving high-accuracy imaging, high-precision well logging identification of hydrocarbon reservoir, ultra-high temperature deep drilling and completion, ultra-high temperature carbonate reservoir stimulation, etc, which solve a worldwide problem that has restricted the exploration of the ultra-high temperature buried hill for many years. These technologies make possible the highest daily production of over 100 m3 oil and 0.5 million m3 gas respectively and sustain a high and stable production for a long term, which guarantee the clean energy supply for Beijing-Tianjin-Hebei region.
Visco-Acoustic FWI for Imaging Beneath Complex Gas Clouds
Authors: Wang Min , Xie Yi , Xiao Bingmu , Ratcliffe Andrew , Latter Thomas
Published: April 2019

The anelastic effects of the earth can cause frequency dependent energy attenuation and phase distortion, especially when gas clouds are present. To correct these unwanted effects for proper imaging, both the velocity and quality factor (Q) models need to be accurately estimated. With FWI offering the capability to obtain higher-resolution models than tomography, visco-acoustic FWI (Q-FWI) is highly desirable for inverting both Q and velocity models together.

The visco-acoustic wave propagation in an anisotropic medium and the gradient computation for model parameters can be implemented in the framework of FWI. However, the similar radiation patterns between velocity and Q make the joint inversion non-trivial (Malinowski et al., 2011; Plessix et al., 2016). Based on the observation that the absorption effects vary with frequency, we can use a range of frequencies to decouple the effects of velocity and Q anomalies. The dispersion relationship helps to reduce the cross-talk between the velocity and Q estimation and provides meaningful updates of these two parameters. The proposed Q-FWI, driven by diving-waves for jointly estimating velocity and Q, is applied on both synthetic and real data to validate its effectiveness.
Study of Ultra-Dry CO2 Foam Fracturing Fluid Enhanced By Graphene Oxide
Authors: Lv Qichao , Li Zhaomin , Zheng Rong
Published: April 2019

Unconventional oil and gas resources such as shale gas, shale oil, CBM, tight gas and oil have attracted more and more attention worldwide in recent years. However, most of the formations of unconventional oil and gas are suffering from poor geological condition, thus the resources can not be developed without fracturing stimulation. Conventional hydraulic fracturing usually consumes a huge amount of water and also leads to the pollutions of surface water and even residential water. In addition, the formation damage caused by incomplete gel breaking, adsorption of polymers, clay expansion and water blocking are still not fully eliminated.

Thus, in this work, ultra-dry CO2 foam stabilized by graphene oxide (GO) were explored to get a fracturing fluid characterized by low water consumption, environmental friendliness, high efficiency and low formation damage. The foam quality of fracturing fluid in the study was higher than 90%, thus the water consumption of fracturing fluid was lower than 10% of total volume. The foam stability, rheology and dynamic filtration were studied by using a large-scale fracturing fluid test device.

The results showed that the stability and thermal adaptability of ultra-dry CO2 foam were enhanced by the addition of graphene oxide. The interfacial dilatational viscoelastic modulus of CO2/liquid was increased when the graphene oxide was used with saponin, implying that the bubble film interface became solid-like; The ultra-dry CO2 foam enhanced by the graphene oxide showed a shear thinning behavior. The effective viscosity of ultra-dry CO2 foam was increased by adding graphene oxide and its viscosity was higher than 50 mPa·s at a shear rate of 100s-1; Moreover, compared to pure surfactant foam, the filtration control performance of ultra-dry CO2 foam was also enhanced by graphene oxide. At a filtration pressure difference of 3.5MPa, the filtration coefficient of ultra-dry CO2 foam was decreased significantly by the addition of graphene oxide. Although the core damage caused by foam with graphene oxide was slightly higher than that of pure surfactant foam, the permeability damage was still below 10%, implying that the foam as a fracturing fluid is relatively clean to formation.

Ultra-dry CO2 foam fracturing fluid stabilized by graphene oxide provides a new high-performance fracturing system for unconventional oil and gas at water-deficient area. This study will be beneficial to fracturing applications characterized by low water consumption, environmental friendliness, high efficiency and low formation damage.
Pressure Transient Behaviour for Alternating Polymer Flooding in a Three-Zone Composite Reservoir
Authors: Kang Xiaodong , Zeng Yang , Zhang Jian , Tang Engao , Wei Zhijie , Cheng Shiqing
Published: April 2019

Alternating high and low concentration polymer flooding has been proposed and applied in some offshore oilfields to improve polymer flooding efficiency, but the research of pressure analysis in alternating polymer flooding reservoir is rare, this work presents a numerical pressure analysis method of three-zone composite model for formation evaluation. The type curves have seven regimes in three-zone composite model. The characteristic is the obvious upturn of pressure derivative curve in transient regime between high concentration and low concentration polymer solution. Formation parameters can be interpreted by history matching and formation evaluation can be conducted based on this model. As an important part of formation evaluation, formation damage as a result of adsorption of polymers in porous media is evaluated by comparing the interpreted permeability with the original value before polymer flooding. The field test data proves that this proposed method can accurately evaluate reservoir characteristics in alternating polymer flooding reservoirs, which emphasizes the potential application of this method in petroleum industry.
Mathematical Model of Acid Wormhole Expansion When Combining Diverting Acid with Solid Diverting Agent
Authors: Li Nianyin , Kang Jia , Zhang Qian , Wu Yu , Zhang Haotian
Published: April 2019

Considering characteristics of complex carbonate reservoirs (e.g., high depth, high temperature, and fracture cave development), this paper simulates expansion of the acid wormhole when combining diverting acid and a solid diverting agent for acid fracturing. Using the theory of reaction kinetics, tests of diverting acid reaction kinetics, and flow reaction experiments on the long core and parallel core, this paper presents tests of the acid–rock reaction for a mathematical model of acid diversion. On the basis of a rheological behavior test of diverting acid, we studied the influences of Ca2+ concentration, pH, fiber concentration, and temperature on acid system viscosity. Then, we established a mathematical model of changes in diverting acid viscosity under a multi-factor cooperative control mechanism. On the basis of the kriging method, we established a three-dimensional (3D) geological model involving a random normal distribution and spatial correlation of multi-fracture and pore-permeability properties. We used four models (acid rock reaction rate, viscosity change, 3D acid wormhole expansion, and fluid–solid coupling) of a complex system to study dynamic cooperation characterization of diverting acid and a solid diverting agent under multiple factors. Simulation results show that the temporary plugging of acid and expansion of acid wormholes are mutually restricted. The solid diverting agent blocked the fracture, and a dense filter cake formed at the start of the fracture; thus, the physical flow direction of diverting acid changed, the acid wormhole length increased, and filtration of diverting acid declined to improve the acid's effect. Diverting acid and solid diverting agent work more effectively together. This paper is novel because we consider the respective influences of Ca2+ concentration, pH, flow rate, diverting acid rheological properties, injection parameters, and solid diverting agent concentration on the synergistic steering of steering acid and a solid diverting agent. We then establish a mathematical model to reflect complex stratigraphic conditions and objectively describe the acid flow reaction. We also innovatively solve the problem of predicting acid wormhole expansion given complex fractures and uneven pore distribution. Findings provide a theoretical basis and technical support for the application of acid fracturing in complex carbonate reservoirs.
Novel Plant-Based Particulate and Fibrous LCM Products for Loss Control while Drilling
Authors: Amanullah Md , Arfaj Mohammed , Alouhali Raed
Published: April 2019

Novel fibrous and particulate LCM products of variable mechanical characteristics and chemical composition were developed using physio-mechanical treatment and processing of various waste components of date palm trees available each year as pruning wastes, post crop harvesting wastes, waste generated after making cookies and confectioneries and also the waste arising due to the removal of deceased and non-productive date trees.

Experimental tests conducted using PPT apparatus at 500 and 1500 psi pressure and 250 °F temperature using 2 mm slotted disc demonstrated the efficient sealing and blocking for all of the particulate and fibrous LCM products either immediately after the application of the overbalance pressure or after a while. Some of the LCM products showed instantaneous sealing capacity after the application of overbalance pressure. Several commercial LCM products were also tested and evaluated using the same concentration and test conditions for comparative assessment of the performance of the newly developed LCM products. Interestingly, all of the new LCM products showed similar or better performance than the equivalent commercial products. Engineered fiber blends developed using the date palm industry waste also showed similar or better performance than the commercial or currently used LCM blends. Experimental results also indicated the suitability of date palm industry waste in replacing some of the synthetic fibers used by the industry in commercial LCM blend design. All these experimental results indicate that the date palm industry wastes are potential sources of raw materials for various product development for oil and gas field applications.
Fluid Discrimination Using Bulk Modulus and Neural Network
Authors: Liu Changcheng , Ghosh Deva , Salim Ahmed Mohamed Ahmed , Chow Weng Sum
Published: April 2019

Hydrocarbon prediction using the rock physical parameters is a common technique in the oil and gas industry. However, the rock physical parameters are controlled by porosity, the volume of clay, pore-filled fluid type and lithology simultaneously. Many methods are proposed to predict the existence of hydrocarbon. This paper proposes a new method ΔK which is the difference between the real bulk modulus and the bulk modulus in the brine- substitute case. The algorithm is validated through stochastic numerical modelling. The brines are separated by the ΔK, and the gas can be detected with acceptable accuracy. Furthermore, a model using deep learning approach is trained to predict the ΔK. The trained model is effective that the predicted values using this model have a strong correlation with the original ΔK. The ΔK can be applied to the data which contains Vp, Vs and density using this approach model. In this study, the ΔK is applied to the Marmousi II dataset to examine the performance and yields a good result. The combination of the deep learning and the ΔK improves our ability in hydrocarbon prediction.
Prediction of Poisson's Ratio for Carbonate Rocks Using ANN and Fuzzy Logic Type-2 Approaches
Authors: Abdulraheem Abdulazeez
Published: April 2019

Young's modulus and Poisson's ratio describe the elastic behavior of rock. It is extremely important to determine these parameters in order to minimize the risk associated with the oil and gas well engineering. The estimation helps in several areas of drilling and production such as well placement optimization, design of completions, mud weight calculations, and hydraulic fracture geometry. Each one of these factors play a part in maximizing the recovery of hydrocarbons and in taking crucial decisions for an appropriate field development strategy.

Poisson's ratio is second most important parameter in understanding the elastic behavior of the rock and plays a critical role in almost all processes such as drilling, reservoir simulation, and production. It is an essential component in geomechanical earth model (GEM).

The Poisson's ratio is estimated based on empirical models and artificial intelligence models. These models are construction from data that has different types of uncertainties. This paper presents an Artificial Neural Network (ANN) as well as Fuzzy Logic Type-2 (FLT2) approach for prediction of static Poisson's ratio. FLT2 is able to incorporate the uncertainties in measurements and still give a robust solution to a given problem. Well log data is used as input and laboratory determined static Poisson's ratio is used as output in the artificial intelligence (AI) tool. The data were collected from a range of experiments conducted on carbonate rocks covering a wide range of input and output values.

The model takes care of uncertainties in the input and output data and is therefore a better approach in establishing a relationship between them and in predicting static Poisson's ratio for new input data.