Indonesia has become a net-oil importer since 2004 as the growing internal demand exceeds Indonesia's oil production. As many fields go into mature phase and combined with other challenges, the national oil production in the last decade has been decreasing from 945 MBOPD to 745 MBOPD with a decline rate of 3-5% per year. Thus, the contribution of the oil and gas sector to the state revenues has also shown a downward trend from 21% in 2010 to only 9.2% in 2019. However, oil production is still strategically important for the national economy. It is important for economic value creation, power generation, transportation, and industries as most of the archipelago's infrastructures are still based on fossil energy. If no effort is made to increase production, the country will be fully dependent on crude oil imports, which poses a threat to national energy security. It is therefore in the nation's great interest to enhance oil production, minimizing the deficit gap between export and import. Several key strategies may be considered to achieve this ambitious target. These strategies can be categorized into the following: 1) People and high performing organization; 2) Exploration, as critical factor for future production; 3) Improved oil recovery (including enhanced oil recovery) technologies, to grow production from the maturing fields; 4) Fast track and simplified project to develop small field discoveries; 5) Strong collaboration between government, industry, academia, and professional associations; and 6)Cost conscious culture. The derivatives of the above-mentioned strategies are among others: standardized resource data management, open source & digitalized geoscience data library, reimbursement system for exploration costs, near field/infrastructure exploration, new play concept, cluster license collaboration, infill wells campaign, multilateral wells, waterflooding, gas injection, stimulation and hydraulic fracturing campaign, well interventions, EOR screening, perfect-well optimization, standardize subsea and/or topside production system, digitalization, and attractive fiscal and regulation that encourages not only the ‘big operator’ to participate in the petroleum sector. The foundation of these strategies should be the legal certainty and effective & proactive bureaucracy. Above all, it is also important to emphasize the common ground of having early HSE involvement as part of the solution. In this paper, the authors would like to contribute in sharing the knowledge, technology and perspectives to all petroleum industry professionals in Indonesia based on the authors exposure in the Norwegian petroleum activities. The paper will also review the strategies, short term and long-term opportunities that may inspire Indonesian petroleum authorities and industry in transforming the ambition into action to achieve the national production target of 1 MMBOPD and 12 BCFD gas by 2030.

Wireline logs have been utilized to indirectly estimate various reservoir properties, such as porosity, permeability, saturation, cementation factor, and lithology. Attempts have been made to correlate Gamma-ray, density, neutron, spontaneous potential, and resistivity logs with lithology. The current approach to estimate grain size, the traditional core description, is time-consuming, labor-intensive, qualitative, and subjective. An alternative approach is essential given the utility of grain size in petrophysical characterization and identification of depositional environments. This paper proposes to fill the gap by studying the linear and nonlinear influences of wireline logs on reservoir rock grain size. We used the observed influences to develop and optimize respective linear and machine learning models to estimate reservoir rock grain size for a new well or targeted reservoir sections. The linear models comprised logistic regression and linear discriminant analysis while the machine learning method is random forest (RF). We will present the preliminary results comparing the linear and machine learning methods. We used anonymized wireline and archival core description datasets from nine wells in a clastic reservoir. Seven wells were used to train the models and the remaining two to test their classification performance. The grain size-types range from clay to granules. While sedimentologists have used gamma-ray logs to guide grain size qualification, the RF model recommended sonic, neutron, and density logs as having the most significant grain size in the nonlinear domain. The comparative results of the models' performance comparison showed that considering the subjectivity and bias associated with the visual core description approach, the RF model gave up to an 89% correct classification rate. This suggested looking beyond the linear influences of the wireline logs on reservoir rock grain size. The apparent relative stability of the RF model compared to the linear ones also confirms the feasibility of the machine learning approach. This is an acceptable and promising result. Future research will focus on conducting more rigorous quality checks on the grain size data, possibly introduce more heterogeneity, and explore more advanced algorithms. This will help to address the uncertainty in the grain size data more effectively and improve the models performance. The outcome of this study will reduce the limitations in the traditional core description and may eventually reduce the need for extensive core description processes.

Over the years, Multiple Array Production Suite (MAPS) has been run several times in Offshore Peninsular Malaysia but never in Offshore East of Malaysia. Field A is located 260km North-North West of Bintulu, Offshore Sarawak and was discovered in 1992 with first gas produced in 2004. One of the many challenges currently faced in managing the field is the prediction and handling of water breakthrough at the existing producers. Based on historical data, water breakthrough from carbonate Zone T begin around 2010 which then followed by series of Water Shut-Off (WSO) campaign. To strengthen the understanding, evaluate the remaining potential and to optimize near term well and reservoir management of the field, an integrated remedial approach is essential. Well-AA was identified for mechanical WSO in an effort to remediate high water production and improve well productivity. The target well was chosen as the well unable to sustain production after a rapid tubing pressure drop due to the highest water production in the field. Moreover, its production had to be capped due to the water production constraints at the receiving hub. Production Logging (PL) was planned across the carbonate sections to accurately identify the appropriate zones for WSO operations. The long horizontal section and high water production typically create a stratified flow regime that forces a smaller volume of hydrocarbon to flow on the high side of the well, hence the conventional PL technology would have been unable to deliver accurate and insightful results. As such, the MAPS technology was run for an initial assessment to identify the water producing zones. MAPS was deployed using wireline tractor and was combined with the Noise Tool (NTO) to provide a comprehensive 3D image of the multi-phase flow profile across the entire wellbore and to investigate the integrity of annular swell packers located in between the carbonate sections. This paper illustrates the best practices involved in the successful downhole Production Logging with a Multiple Array Production Suite and Digital Noise Tool (PL-MAPS-NTO) toolstring, which served as the key input in determining the WSO treatment depth and strategy in Well-AA, that may lead to a potential gain of 10.8MMscf/d.

Manifestation of Low Resistivity Pay (LRP) Existences in ONWJ Area because of Fine Grained, Superficial Microporosity, Laminated Shaly Sand and Electronic Conduction. Water saturation petrophysical analysis for LRP Case due to those reason above can be solved by electrical parameter determination with Type Curve. But to overcome the LRP caused by Laminated Shaly Sand, the use of high resolution resistivity logs that are close to the resolution of thin bed reservoir is a must. Alternative solutions, conventional high resolution resistivity logs, namely Micro Spherical Focused Log (MSFL) are used to interpret thin bed reservoirs that have the hydrocarbon potential. This intergrated petrophysical analysis is called MAINE Petrophysical Method The Petrophysical MAINE method is the development of the TECWAL (Type Curve, Core and Water Analysis) method which leaves question marks on Laminated Shaly Sand Reservoir and the possibility of variations in the Electrical Parameter and Water Saturation Irreducible (SWIRR) dependent on Rocktype. The Basis of the MAINE Method is the Worthington Type Curve with some assumptions such as Each rocktype has a different value of Bulk Volume of Water (BVW) and BVW can be used to determine the SWIRR value of each rocktype and Each rocktype has different electrical parameter m and n. In the process, the use of J-Function and Buckles Plot is applied to help determinet Rocktype and BVW values. The rocktype will be the media in distributing the value of Electrical Parameter generated by the Type Curve and the value will be used in water saturation calculation. In Laminated Shaly Sand Reservoir, Rocktyping will be analyzed more detail using the High Resolution Conventional Log, Micro Spherical Focused Log (MSFL). The expected final result of this analysis is the more reliable Water Saturation (SW) and the integration of water saturation values in the Buckles Plot which can help in determining the transition zone in order to avoid mistakes in determining the perforation zone. Through the MAINE Petrophysical Method, there is a decrease in water saturation from an average value 86% to 66% or a decrease 23%. This result is quite significant for the calculation of reserves in the LRP zone. By integrating this method with the Buckles Plot, it can help the interpreter to determine the perforation interval in order to avoid water contact or the transition zone

The calibration of shaly-sand reservoir is challenging since the nature of geological complexity of the reservoir. This complex structure involves multiple scales that should be acknowledged during geologic and reservoir modeling activities. This paper is intended to show multi-scale response of shaly-sand reservoir, by integrating well, sector, and reservoir data. Reservvoir modeling is used as a tool to understand the concept and behaviour of shaly sand reservoir under multiple scenarios of shale geological setting and shale configuration. The research is based on day-to-day findings in PHE ONWJ working area where drilling activities often encounter zones with very low water saturation or high pressure, even though the infill drilling is performed nearby depleted zones. This work demonstrates the needs of multiscale integration to analyze shaly-sand reservoir response. The geology of shaly-sand reservoir indicates "compartment" behavior. The interbedded shale layers disconnect the continuity of several layers. The global scale data, e.g. average reservoir pressure, cannot accurately capture the local responses and discontinuities. Therefore, huge amount of oil reserves becomes undetected and undeveloped. Reservoir characterization based on Field X in PHE ONWJ area is used as a benchmark in modeling a generic reservoir model. The model utilizes several shale configuration and shale characteristics in order to mimic shaly sand reservoir behavior during a single primary production cycle. Whilst general production resultsis not the main concern of the current publication, The main goal of the publication is to observe pressure behaviour after several years of primary production. The research provides a new insight on how field development plan should be prepared accordingly should there be a conviction of shaly sand reservoir from test data. Developing shaly sand reservoir should require multiple plans for higher number of infill well as well as its placement and economic aspects.

M field is a faulted anticline structure that lies in a deepwater turbidite environment. Field Development started in late 2016, with 10 oil producers and 2 water injectors. Within 2 years of production, significant GOR increase in some of the wells led to production curtailment, which has impacted the field production promise. Poor injectivity seen in one of the water injectors also led to an assumption of compartmentalization or sandface plugging/damage that required investigation. In order to evaluate intervention opportunities to mitigate against the high GORs and to determine the cause of the poor injectivity, production logging tools were proposed in four candidate wells. The objectives of the logging campaign were: To understand the gas influx profile into the well and how different compartments are contributing to the GOR Assess behaviour of G/O, O/O, O/W crossflows and their impact on reservoir depletion profile Aid decisions in requirements for downhole controls (i.e. AICD placement) vs surface controls Provide opportunity to couple PL logs with PNC logs to identify potential GOCs, estimate gas/water saturations and determine if there are any bypassed oils Based on the candidate wells, the following challenges were present: Highly-deviated/horizontal wells requiring complex conveyance solutions Multiphase flows (gas, oil and potentially water) in highly deviated conditions which further complicates fluid phase contribution calculations and velocity modeling Rig up height limitations on the platform which requires shorter logging tool strings High flowrates with tool lift limits requiring careful modelling work to ensure risks are understood and minimized In view of these challenges, a new Array Production Logging Tool (henceforth called New-APLT) was proposed as an alternative to the previous generation APLT. It has a more robust design with co-located sensors in a single module with additional optical sensors that improves flow measurement and gas detection. Additionally, screen tracers sampling was proposed in one well, which would help calibrate the tracer interpretations against actual fluid rates. The novel approach and synergistic efforts amongst many disciplines led to a successful execution of the logging campaign, and the first ever deployment of the New-APLT tool on e-line tractor. The timely results from the campaign which coincided with a 4D seismic acquisition has helped to justify downhole control options for some of the wells, and potentially helped to avoid costly remedial work on the water injector. The valuable dataset will also influence the infill development well campaign location, design and well count.

This study presents a data-driven approach using machine learning algorithms to provide predicted analogues in the absence of acoustic logs, especially while drilling. Acoustic logs are commonly used to derive rock mechanical properties; however, these data are not always available. Well logging data (wireline/logging while drilling - LWD), such as gamma ray, density, neutron porosity, and resistivity, are used as input parameters to develop the data-driven rock mechanical models. In addition to the logging data, real-time drilling data (i.e., weight-on-bit, rotation speed, torque, rate of penetration, flowrate, and standpipe pressure) are used to derive the model. In the data preprocessing stage, we labeled drilling and well logging data based on formation tops in the drilling plan and performed data cleansing to remove outliers. A set of field data from different wells across the same formation is used to build and train the predictive models. We computed feature importance to rank the data based on the relevance to predict acoustic logs and applied feature selection techniques to remove redundant features that may unnecessarily require a more complex model. An additional feature, mechanical specific energy, is also generated from drilling real-time data to improve the prediction accuracy. A number of scenarios showing a comparison of different predictive models were studied, and the results demonstrated that adding drilling data and/or feature engineering into the model could improve the accuracy of the models.

This paper describes how active and passive magnetic ranging logging used while drilling subsurface intervention wells shows characteristics of the target well casing integrity and damage. Over the course of the development of a novel active magnetic ranging system and through several years of commercial application, data has been collected and analyzed to understand the characteristics of casing damage. This paper explains the methods used in field operations to collect this data. Using the gathered information, various stages of casing damage and poor integrity are shown. Results obtained from active and passive magnetic ranging are presented in the context of identifying casing damage. This is a departure from the standard methods of interpreting the data as it is not focused on locating a wellbore but determining the integrity of the casing. Casing integrity in idle wells is usually understood by conventional logging techniques until there is a restriction or damage on the well. Magnetic ranging logging performed during the intervention to abandon these wells can give an indication to operators of the casing integrity that otherwise would have been unknown without access to the damaged well. This can help optimize subsequent abandonment procedures as well as assist with field planning into the future to mitigate issues stemming from casing integrity and to identify the causes of previously unknown critical casing damage. The paper reports surface experimental data and compares it with two field examples. In the first field example, the passive magnetic interference from a hundred-year-old casing in the offset well caused more than 100000nT deviation from the reference field approximately 1ft away from the offset well, suggesting severe casing damage. The active magnetic signature measured simultaneously approaches zero, pointing to a lack of electrical continuity in the offset casing caused by a complete break. The second field example shows an offset well segment with passive interference of 7000nT in the presence of a stable active magnetic signal at approximately 2ft separation between wells due to possible casing damage without complete separation. The passive interference increases to 14000 nT at deeper depth while the active signal approaches zero due to a complete casing break. Novel application using the data collected by active and passive magnetic ranging techniques is being applied for the understanding of issues related to casing integrity.

D31 cluster is located in the prolific Mumbai Offshore Basin, Western part of India. B-192Afield is part of this cluster, where Bassein (Middle Eocene),Mukta and Panvel (Early Oligocene)Formations are the main reservoirs. The reservoirs are complex in terms of reservoir heterogeneity. They were deposited in a shallow marine carbonate platform.Sea level fluctuations andchange in depositional environment impacted the porosity development. The main objective of this study was to integrate spectral gamma ray signatures with seismic interpretation for demarcating significant stratigraphic surfaces and differentiating depositional environments for robust reservoir characterization. Regionally, Bassein Formation (Middle Eocene) is characterized by thick foraminiferal and algal wackestone, packstone and occasional grainstone facies.The Mukta Formation (Early Oligocene),which unconformably overlies the Bassein Formation, is characterized by presence of fossiliferous limestone with shale intercalations. In the present study, data from four exploration wells data have been analyzed, where spectral gamma ray log patterns in carbonate reservoirs appear to have a distinctive relationship to depositional facies and stratigraphic surfaces in the Bassein and Mukta Formations. Different cross plots have also been utilized for analyzing the depositional conditions (i.e. oxic or anoxic).Later, the spectral log interpretations have been integrated with seismic interpretation. This study is part of a larger effort for reservoir characterization, as a basis for seismic interpretation and integrated reservoir modelling. The spectral gamma ray signatures demarcated significant stratigraphic surfaces. In BasseinFormation, three different units have been marked as Upper, Middle and Lower Bassein. The major lithological boundary between the Bassein and Mukta Formation is also well demarcated with spectral GR signature. The carbonate strata of Bassein & Mukta Formation have also been subdivided with U-Th-K abundance.The "Low Th-Low U" units indicative of pure carbonate and deposition in oxidizing environment whereas "Low Th-High U"is indicative ofreducing environment, which gave a relative sea level fluctuation in the area.The major stratigraphic boundaries identified from these spectral GR logs has been incorporated in the seismicinterpretation and used for regional seismic mapping.As porosity development is governed by thesea level fluctuations,this study also gave an indication of the possibility of porous zonein the reservoir section. These results can be useful as a basis for applying spectral GR signature as a tool for stratigraphic interpretation in un-cored heterogenous carbonate sections. Along with the petrophysical interpretation, integration of core analysis, biostratigraphy and seismic attribute are critical for detailed carbonate reservoir characterization incorporating depositional environment.This approach can be applied to support commercial development of the complex carbonate reservoirs.

Grain size characteristics (d50, UC) of formation sands are crucial parameters in a sand control design. UC and d50 are commonly derived from sieve or laser particle size analysis (LPSA) techniques on a limited number of core samples in the process of drilling, which cannot represent the variations of grain sizes in the formation by the limited number of core samples. Moreover, staged and hierarchic design of sand control usually needs the whole longitudinal distribution profile of grain size. The grain size characteristics of the reservoir are formed in the process of a long history and have a good correlation with the formation environment of the sediments. Sand control design can only use test well data, because of lacking actual producing position cores. The vertical and horizontal anisotropy and heterogeneity of reservoirs bring difficulties and greater risks to the design of sand control schemes. Therefore, it is very important to find a simple and effective reservoir granularity prediction method. The existing prediction models by artificial intelligence method use single point logging data as eigenvalues to predict d50 and UC without considering the longitudinal continuity of data. This paper presents an efficient solution to predict grain size profile based on conventional logging curves by using four machine learning method (ANN, Random forest, XGBoost, SVM). In order to make full use of the geological continuity of the reservoir, the longitudinal continuous points according to the spatial correlation are adopted as the machine learning feature parameters from the perspective of geological analysis and the data-driven grain size profile prediction model are established by using the logging curve trend and background information, which further improves the prediction accuracy of the model and provides basic data for sand control. The ANN model of five point mapping has the best prediction effect in predicting d50 with a highest correlation coefficient 0.819 and a lowest error MAE 9.59. The XGBoost model of five point mapping has the best prediction effect in predicting UC with a highest correlation coefficient 0.402 and a lowest error RMSE 1.15. This method has been successfully used in offshore oil field in sand control optimization.

Freshwater environment and high clay content are quite common in Indonesia. This introduces certain challenge in performing hydrocarbon identification and evaluation especially in already cased wells. In old producer wells, possible conditions such as fluid channeling behind casing and trapped hydrocarbon in annulus add more complexity in performing behind casing analysis to understand current reservoir condition. In order to increase the success in finding remaining hydrocarbon potential, PERTAMINA has deployed pulsed neutron logs (PNL) to accurately pinpoint the targeted interval for perforation. Since 2017, the PNL campaign has covered approximately 160 wells in PERTAMINA's development fields across Indonesia up until now. PNL service offers nuclear-based statistical measurement such as sigma, thermal neutron decay porosity (TPHI), and carbon-oxygen yield that allows simultaneous oil and gas saturation evaluation without any dependence on water salinity and other electrical properties of the formation and fluid. It also allows computation of elemental dry weight from elemental spectroscopy data which can be utilized to determine lithology to complement the standard open-hole logs dataset. The more advanced PNL tool raises the bar even further by offering new measurement of fast neutron capture cross section (FNXS) log which is useful to identify gas even in tight rock formation. The latest generation also features self-compensation algorithm resulting in more robust TPHI and sigma log under complex circumstances such as multi-casing/tubing. This paper showcases several prominent success stories of oil and gas findings identified from PNL interpretation in development wells. There are also several examples of elemental spectroscopy data utilization from PNL to prevent non-economical perforation by means of providing accurate lithology and porosity analysis as compared to previous result built from old and/or incomplete open-hole logs dataset. This PNL campaign has also given valuable insights of borehole and reservoir condition which might have been overlooked such as hydrocarbon in annulus, low pressure gas zone identification and batman's ear boundary effect. Low pressure gas zone may be qualitatively identified whenever TPHI from PNL is noticeably lower than neutron porosity measurement from the open-hole log. Batman's ear effect is usually observed when a body of sand is sandwiched between carbonaceous shales or coal layers resulting successive oil-water-oil saturation profile in one homogenous body of sand, shown as oil peaks at the bed boundaries similar with the appearance of batman's ear. As the sand gets thinner, these two oil peaks might merge into one solid body of high oil saturation which might not depict the true oil potential of the sand.

While drilling into fracture zones, lost circulation frequently occurs, resulting in a waste of productive operation severe cases, the well's destruction. However, due to complex development mechanisms and high heterogeneity, identifying and predicting fractures is extremely difficult. This study proposes a new drilling loss prevention idea to evaluate fractured lost circulation risk using seismic and wellbore data by a novel neural network. The approach works in two steps. First, the fracture anisotropy of a lost circulation sample curve is computed and interpreted using well logs. Second, using seismic attributes as constraints, a novel neural network is used to develop a prediction model. The field application in the Sichuan basin verifies the method's efficacy and confirms the method's ability for predicting lost circulation probability both along the well trajectory and in regions away from the drilled wells.

"Intrabeta" is a subzone located in the upper part of Tunu Main Zone between MF3-MF6 regional stratigraphic marker. Total cumulative production from this subzone is at 51 Bcf of gas and 4.96 MMBbl of oil. This interval is situated between Tunu Shallow Zone and Tunu Main Zone, which are the main producing intervals of Tunu Field, a giant mature gas field in Mahakam Delta, Indonesia. With Intrabeta reservoir depositional context more dominated by channel facies and more varied production fluid properties, the development of Intrabeta subzone became more challenging and previously classified as upsides. As Tunu Field is getting more matured, the challenge to deliver infill wells that economically profitable become more arduous. Thus, all attempts to give additional value to the future infill wells should be properly assessed. This paper aims to provide a comprehensive summary of how strategic collaboration between static and dynamic synthesis of Intrabeta subzone has given additional contribution in Tunu Field continuous value creation process. The method started by conducting an extensive post-mortem review on all perforated reservoirs in Intrabeta subzone. Insights from the perforated reservoirs that comprise of production behavior, perforation success ratio, cumulative hydrocarbon production and updated portfolio are then utilized to provide initial prolific area map for future candidates maturation. Data coming from the dynamic analysis were then combined with static depositional analysis on how the hydrocarbon was distributed in Tunu Intrabeta subzone. A new structural map that has been corrected from seismic push-down effect due to shallow gas presence above Intrabeta interval was then utilized to map the structurally promising area. Deterministic channel boundaries and possible sweet spots are then identified and ranked based on the development confidence level. Four wells with additional stakes from Intrabeta subzone have been proposed and drilled in Tunu Main Zone. All wells have successfully found the targeted Intrabeta targets with various post-mortem findings. While in some wells significantly better post-drilling results were encountered, in other cases slightly lower results were found due to static channel development and fluid dynamic uncertainties. All the lesson learned gathered from the pilot wells provide valuable insights on future improvement toward better and more robust Intrabeta candidate maturation methodology. The insights gained from this study have given essential understanding of Tunu Intrabeta subzone characteristics and possible future potentials. Furthermore, this paper provides a comprehensive summary, systematical approach and lesson learned in enhancing previously upside potential of Intrabeta subzone in Tunu Main Zone to compelling additional targets in Tunu Main Zone future wells as part of the continuous value creation process in a giant mature gas field.

A brown field, offshore Sarawak, Malaysia, with multiple sub-layered laminated sands of varied pressure regimes and mobility ranges, was challenged by depletion, low mobility and uncertainty in the current fluid types and contacts. Optimal dynamic fluid characterization and testing techniques comprising both Wireline and Logging While Drilling (LWD) were applied in nine development wells to acquire reliable formation pressure data and collect representative fluid samples including fluid scanning. Some of the latest technologies were deployed during the dual crises of falling oil price and the Covid-19 pandemic. The S-profile wells were drilled using oil-base mud (OBM) with an average deviation of 60 degrees. Formation Pressure While Drilling (FPWD), Fluid Sampling While Drilling (FSWD) and wireline formation testing, and sampling were all utilized allowing appropriate assessment of zones of interest. Various probe types such as Conventional Circular, Reinforced Circular, Elongated, Extra-Elongated and Extended Range Focused were used successfully, ensuring that the right technology was deployed for the right job. Formation pressure and fluid samples were secured in a timely manner to minimize reservoir damage and optimize rig time without jeopardizing the data quality. As a classified crisis due to the pandemic, rather than delaying the operations, a Remote Operations Monitoring and Control Center was set-up in town to aid the limited crew at rig site. A high success rate was achieved in acquiring the latest formation pressure regimes, fluid gradients, scanning and sampling, allowing the best completion strategy to be implemented. With the selection of the appropriate probe type at individual sands, 336 pressure tests were conducted, 44 fluid gradients were established, 27 fluid identification (fluid-id / scanning) pump-outs were performed, and 20 representative formation fluid samples (oil, gas, water) were collected. Amongst the Layer-III, Layer-II and Layer-I sands, Layer-I was tight, with mobility < 1.0 mD/cP. Wireline focused probe sampling provided clean oil samples with 1.4 to-3.7 wt. % OBM filtrate contamination. The water samples collected from Layer-II during FSWD proved to be formation water and not injection water. The wells were thus completed as oil producers. Reliable fluid typing and PVT quality sampling at discrete depths saved rig time and eliminated the requirement of additional runs or services including Drill Stem Testing (DST). This case study has many firsts. It is the first time where latest fluid characterization and testing technologies in both Wireline and LWD were deployed for an alliance project in Malaysia and that too during dual crises of falling oil price and the pandemic aftermath. Overcoming various challenges including limited rig site manpower, there was no delay in completing the highly deviated wells with tight formations in a single drilling campaign and provided rig time savings. For the purpose of this case study, two wells have been discussed. First well used the wireline focused sampling technology and the second used the FSWD technology.

Maximizing reservoir contact through extended-reach and mega-reach wells has become a prevalent field development strategy for major offshore operators in the Middle East. This is especially true for the giant oilfield "A", where drilling targets go beyond 40,000 ft. measured depth (MD), with MD/TVD ratios in excess of 4.5:1. Such challenging horizons call for a detailed re-evaluation of well interventions. In 2019, the well surveillance program in the field A required intervention in a mega-reach well with a MD over 35,500 ft. and 4.5:1 MD/TVD ratio. This reach was unthinkable only a few years ago but has been made possible thanks to several recent key technological advancements, such as coiled tubing (CT) equipped with optical fiber and new CT hydraulic tractors, proactive and detailed planning during the drilling phase, the development of highly engineered CT string designs, surface equipment upgrades, and accurate software modeling. The target well is an oil producer with horizontal section beyond 23,000 ft., completed with 6 5/8-in. pre-perforated liner and 23 swellable packers placed across the 8 1/2-in. open hole section. A multiphase production logging tool was selected to assess the production profile along its horizontal drain. With a target depth beyond the reach of conventional wireline, CT equipped with optical fiber emerged as the optimum solution to facilitate reach and overcome the weight and pumping limitations of wired CT. A comprehensive CT reach modeling exercise compared the performance of several 2-in. and 2 3/8-in. CT string designs and identified operational requirements and reach gains from CT hydraulic tractors. As a result, an engineered 2-in. CT tapered string of near 36,700 ft. was developed, capable of being equipped with optical fiber line, while delivering the required flow rate and differential pressure to the CT hydraulic tractor without compromising any operational safety margin. At the time of manufacturing, this was considered the longest CT string ever produced and fitted for downhole telemetry. The operation itself set new records for well interventions in mega-reach wells, with a CT reach above 35,500 ft. MD, including a hydraulic tractoring footage over 15,650 ft. MD with spaced slugs of chemical friction reducer. This case study explains how to develop a safe, robust, and effective solution to mega-reach well challenges using the CT-conveyed optical fiber telemetry technology in one of the deepest wells in the field A, setting a new global record in CT reach. The lessons learned are now the reference for other operators in the Middle East and across the globe for performing interventions in wells that continue to be stretched in its extended reach. It also depicts why telemetry through optical fiber is key to the success of such projects and provides an overview of technology needs for the future of mega-reach well developments.

Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent ( Figure 1 ). The learning rate is usually selected as less than one.

Application of diversion agents in temporarily plugging fracturing of horizontal wells of shale has becoming more and more popular. Nevertheless, the studies on determining the diverter dosage are below adequacy. A novel approach based on laboratory experiments, logging data, rock mechanics tests and fracture simulation was proposed to optimizing the dosage of diversion agents. The optimization model is based on the classic Darcy Law. A pair of 3D-printed rock plates with rugged faces was combined to simulate the coarse hydraulic fractures with the width of 2.0 ~ 7.0 mm. The mixture of the diversion agents and slickwater was dynamically injected to simulate the fracture in Temco fracture conductivity system to mimic the practical treatment to temporarily plugging the fracture. The permeability of the temporary plugging zone in the 3D-printed fractures was measured in order to optimize the dosage of the selected diversion agents. The value of P net (also the value of ΔP in Darcy Formula) required for creation of new branched fractures was determined using the Warpinski-Teufel Failure Rules. The hydraulic fractures of target stages were simulated to obtain the widths and heights. The experimental results proved that the selected suite of the diversion agents can temporarily plug the 3D-printed fractures of 2.0 ~ 7.0 mm with blocking pressure up to 15 MPa. The measured permeability of the resulting plugging zones was 0.724 ~ 0.933 D (averaging 0.837 D). The value of P net required for creation of branched fractures in shale of WY area (main shale gas payzone of China) was determined as 0.4 ~ 15.6 MPa (averaging 7.9 MPa) which means the natural fractures and/or weak planes with approaching angle less than 70° could be opened to increase the SRV. The typical dosage of the diversion agents used for one stage of the horizontal wells (averaging TVD 3600 m) was calculated as 232 ~ 310 kg. The optimization method was applied to the design job of temporarily plugging fracturing of two shale gas wells. The observed surface pressure rise after injection of diversion agents was 0.6 ~ 11.7 MPa (averaging 4.7 MPa) and the monitored microseismic events of the test stages were 37% more than those of the offset stages.

The PEP structure in Matindok block in Central Sulawesi has been proven to produce gas reserves in the Minahaki formation based on the first exploration well PEP-001 that was drilled in 2018. The PEP structure has a Miocene carbonate build-up play, and the target reservoir is the M member pinnacle carbonate reef. One of the main challenges in this area is low resolution of seismic data, leading to a high depth uncertainty of top M position. The PEP-001 well was planned to set the 9 ⅝-in casing point above top M. Offset wells did not show any clear markers in the thick shale above M formation, that could have been used for log correlation. In previously drilled offset wells correlation was done conventionally by taking cutting samples and relying on drilling parameters break. However, when PEP-001 was drilled, no apparent drilling break was observed. By the time cuttings reached surface, the bit had drilled into 20-m thickness of M formation. Since casing covered most part of the upper carbonate formation, open hole logging and well testing data were not acquired to delineate target M formation optimally. The second exploration well PEP-002 was planned with an objective to set 9 ⅝-in casing ~5 m above top M to acquire a full interval coverage of coring, open hole wireline logging, and well testing program. This information was critical for optimal reservoir delineation to allow for accurate reserve calculation and future structure development. Conventional correlation (well-seismic pairing and correlation) has proven insufficient for casing point placement and was a lesson learned from PEP-001. The presence of limestone stringers observed in offset wells within proximity of top M presented an additional challenge. The stringers could have been mis-interpreted as the main carbonate body, if interpretation was solely based on cutting samples. Based on these challenges, a technology with the capability to map and detect lithology changes ahead of the bit in real time was required. Real-Time EM Look Ahead technology uses deep directional electromagnetic (EM) technique to detect formation feature ahead of the bit. A feasibility study was done to simulate the tool response and define the placement of transmitter and receivers (spacings) in the BHA, as well as frequency selection based on the resistivity properties from offset wells. Based on the simulation, this technology was expected to detect top M formation as early as 10 m ahead of the bit. While drilling, top M could be resolved at 6 m ahead of the bit with an uncertainty of <1 m, therefore making this the fit-for-purpose technology to place the 9 ⅝-in casing point ~5 m above the M formation. As a result, 9 ⅝-in casing was successfully placed 5 m above top M. High resistivity contrast, that is expected to be top M, was mapped continuously from 10 m ahead of the bit. The decision was to set the casing point at 1706 m MD, 4-5 m from the estimated top M. Coring results and open hole logging in the subsequent 8 ½-in hole section confirmed that top M was at 1711 m MD, 5 m from the casing point, which was precisely as estimated from Real-Time EM Look Ahead Technology. This technology also helped detect thin limestone stringer and differentiate it from the target carbonate formation. It prevented 9 ⅝-in casing to be set ~118 m shallower where the first limestone stringer was observed. This has avoided extra operating days in case of drilling problems due to long exposed shale in the subsequent 8 ½-in hole section. The potential cost savings were estimated at USD 1.35 million for an additional 6-in hole section. Following this success, the same technology will be used in the next planned exploration wells.

The strong domestic need for oil in China requires further exploration in unconventional reservoirs, such as volcanic and shale oil reservoirs. Sweet zone identification is one of the most critical missions in formation evaluation. The complex mineralogy and the low porosity in unconventional reservoirs result in little contrast of resistivities between oil-producing zones and water zones. Reducing uncertainties affecting the type of fluid in the reservoir and its potential movability is key to defining the optimal landing zones for future horizontal drains. In conventional reservoirs, nuclear magnetic resonance (NMR) logging based on cutoff analysis is the optimum choice for evaluating the porosity and pore geometry of hydrocarbon bearing reservoirs. However, the routine single-dimension T2 measurement is not sufficient for fluid typing in unconventional reservoirs because of the overlap of signals of various fluids in the T2 domain. This contribution presents case studies from a new generation NMR tool providing continuous 2D T1-T2 measurements. The T1-T2 measurements enable separation and quantification of different fluids in the pores. Fluid typing can be done by integration of other wireline logs such as spectroscopy and dielectric. Case studies are presented from volcanic and shale oil reservoirs in Xinjiang Oilfield of PetroChina. In the volcanic reservoir case, dedicated 2D data analytics technic is used to extract relevant signals from 2D NMR T1-T2 measurements. An integrated workflow combining wireline borehole electrical image, nuclear spectroscopy, dielectric dispersion and NMR T1-T2 gives an insight into the fluid composition in the pore system. Besides accurate measurements of lithology independent porosity and pore geometry from NMR, T1-T2 specific measurement of clay bound water volume and total water volume match well with the same measurements estimated from nuclear spectroscopy and dielectric dispersion logs. It lowers the uncertainties on fluid types in the volcanic reservoir where resistivities fail to differentiate wet zones and oil zones. In the shale oil reservoir case, a reservoir producibility index (RPI) derived from the integration of NMR T1-T2, wireline borehole electrical image, and nuclear spectroscopy is shown to be quite efficient in sweet zone identification and ranking. This paper discusses a novel application of a new generation NMR T1-T2 logging method in unconventional reservoirs, which helps the operator ascertain the potential of these reservoirs. The oil zones identified by the new method have promising oil productions. The workflow illustrated in the two case studies can be applied and extended to other unconventional plays in China.

One of the keys to successful and environmentally responsible well stimulation programs in coal seam gas development is to establish consistent procedures for the safeguarding, planning and executing activities across multiple wells. The aim of this paper is to show how a novel application of petrophysical program scripting can be used to make the stimulation process more efficient, consistent and compliant across assets with varying requirements. A macro embedded in petrophysical evaluation software applies a series of rules to rank coals by thickness, allocate a series of perforations and stimulation type based upon coal rank and spacing and then produces actionable treatment schedules which are seamlessly implemented in well stimulation operations at well sites. To do this, the macro grades all coals within the well by thickness based upon a cut-off on the density log, with the thickest coal being graded highest. The macro then identifies the top ranked coal and places perforations based on user defined logic, geological information from offset wells, permeability attributes of the target coal layer(s), depth and vertical separation between adjoining coal targets. Based on the stimulation type assigned, a stimulation schedule is generated that includes estimates of fluid volumes, proppant volumes, injection rates, proppant ramp type and stipulates flush conditions (over-flush or under-flush). Coals thicker than a maximum perforation size are perforated in an upper, middle and lower configuration. Most coals are thinner than the maximum allowable perforation interval and so the macro looks up and down the borehole to include thinner coals within a potential perforation window. The system then generates the stimulation schedule as described above. The macro continues to allocate perforations and stimulation schedules for each validated coal interval and sequentially tries to maximise the total target coal interval along the wellbore. Certain environmental constraints are included in the macro logic to maintain local and regional commitments. For example, coal zones in proximity of permeable non-coal layers i.e. interburden are automatically excluded from stimulation. Multiple advantages of this system have been realised including, a) effective QA/QC as outputs can be directly plotted against the well logs giving the user a quick and easy visual check b) actionable instructions that site based teams can execute including exact perforation depths and stimulation schedules c) provide realistic materials and costs estimates that ensure efficient planning and logistics, d) monitor and document any variations between allocated schedule versus actual execution, e) provide estimate of expected net coal connectivity at a well, development package and asset level which feeds into production and recovery forecasts, f) plan future optimisation studies or pilots and g) most importantly offers a consistent, efficient and compliant framework that can be applied across multiple assets, engineering teams and service providers. This paper focuses on capabilities and advantages of using a macro to automate stimulation design allocation for CSG multi-well (>100 wells) assets. Details of individual stimulation designs for Walloons Coal measures are mentioned in other publications ( Kirk-Burnnand et al., 2015 and Flottmann et al. 2018 ) and hence not covered here.