Decline curve analysis is widely used in industry to perform production forecasting and to estimate reserves volumes. A useful technique in verifying the validity of a decline model is to estimate the Arps decline parameters, the loss ratio and the b-factor, with respect to time. This is used to check the model fit and to determine the flow regimes under which the reservoir produces. Existing methods to estimate the b-factor are heavily impacted by noise in production data. In this work, we introduce a new method to estimate the Arps decline parameters. We treat the loss ratio and the b-factor over time as parameters to be estimated in a Bayesian framework. We include prior information on the parameters in the model. This serves to regularize the solution and prevent noise in the data from being amplified. We then fit the parameters to the model using Markov chain Monte Carlo methods to obtain probability distributions of the parameters. These distributions characterize the uncertainty in the parameters being estimated. We then compare our method with existing methods using simulated and field data. We show that our method produces smooth loss ratio and b-factor estimates over time. Estimates using the three-point derivative method are not matched with data, and results in biased estimates of the Arps parameters. This can lead to misleading fits in decline curve analysis and unreliable estimates of reserves. We show that our technique helps in identification of end of linear flow and start of boundary dominated flow. We use our method on simulated data, with and without noise. Finally, we demonstrate the validity of our method on field cases. Fitting a decline curve using the loss ratio and b-factor plots is a powerful technique that can highlight important features in the data and the possible points of failure of a model. Calculating these plots using the Bourdet three-point derivative induces bias and magnifies noise. Our analysis ensures that this estimation is robust and repeatable by adding prior information on the parameters to the model and by calibrating the estimates to the data.

Application of horizontal drilling and hydraulic fracturing technique has made development of shale gas reservoir successful in the United States during the past decade. Chasing its operational success, researchers have been studying to understand the fundamentals of shale gas production, which will provide valuable information to assist in optimization of shale reservoir development. Unfortunately, the mechanism of shale gas production has not been fully revealed so far, and most reservoir simulation models are adopting the mechanism of coalbed methane production to forecast shale gas development process, which might not be the real case. In this paper, instead of using numerical simulation model, artificial intelligence and data mining techniques are implemented to study the controlling factors of shale gas production and understand the impacts of reservoir, completion and stimulation parameters in a dynamic manner only according to the field data. A database of Marcellus shale reservoir is generated by integrating information such as well locations, well trajectories, reservoir characteristics, completion, hydraulic fracturing, and production parameters, etc. Neural network models are trained to learn the key performance impacting factors on shale gas production in a dynamic manner, which could assist reservoir management decisions.

This paper presents a method for examining the potential of fracture stimulation in horizontal wells targeting unconventional oil plays. The observation of crossflow among fractures has been of great concern as this phenomenon affects the productivity of producing wells. The cause is related to the effectiveness of fracturing stages, which by itself depends on the rock lithology and brittleness. We identified interaction among fractured intervals from diagnostic modeling of performance data that exhibited cross flows in the wellbore. On wells exhibiting the most prolonged duration of crossflow, we noted the disadvantages of equal space fracturing. We then used the drilling parameters from MWD data for individual wells and computed the d-exponent profiles and noted significant differences in rock brittleness as characterized by their d-exponent data. We examined the d-exponent data using the Hurst coefficient. Wells exhibiting a trend with uniform H coefficient showed the least indications of cross flows from performance data while in wells with significant cross flows we see the nonuniformity of the d-exponent profile examined with the H coefficient method. It is our observation that before fracturing, interval selection with GR log can be enhanced if supplemented with the trend analysis of the d-exponent data.

Drilling problems such as stick slip vibration/hole cleaning, pipe failures, loss of circulation, BHA whirl, stuck pipe incidents, excessive torque and drag, low ROP, bit wear, formation damage and borehole instability, and the drilling of highly tortuous wells have only been tackled using physics-based models. Despite the mammoth generation of real-time metadata, there is a tremendous gap between statistical based models and empirical, mathematical, and physical-based models. Data mining techniques have made prominent contributions across a broad spectrum of industries. Its value is widely appreciated in a variety of applications, but its potential has not been fully tapped in the oil and gas industry. This paper presents a review compiling several years of Data Analytics applications in the drilling operations. This review discusses the benefits, deficiencies of the present practices, challenges, and novel applications under development to overcome industry deficiencies. This study encompasses a comprehensive compilation of data mining algorithms and industry applications from a predictive analytics standpoint using supervised and unsupervised advanced analytics algorithms to identify hidden patterns and help mitigate drilling challenges. Traditional data preparation and analysis methods are not sufficiently capable of rapid information extraction and clear visualization of big complicated data sets. Due to the petroleum industry's unfulfilled demand, Machine Learning (ML)-assisted industry workflow in the fields of drilling optimization and real time parameter analysis and mitigation is presented. This paper summarizes data analytics case studies, workflows, and lessons learnt that would allow field personnel, engineers, and management to quickly interpret trends, detect failure patterns in operations, diagnose problems, and execute remedial actions to monitor and safeguard operations. The presence of such a comprehensive workflow can minimize tool failure, save millions in replacement costs and maintenance, NPV, lost production, minimize industry bias, and drive intelligent business decisions. This study will identify areas of improvement and opportunities to mitigate malpractices. Data exploitation via the proposed platform is based on well-established ML and data mining algorithms in computer sciences and statistical literature. This approach enables safe operations and handling of extremely large data bases, hence, facilitating tough decision-making processes.

In the late summer of 2009 an American oil and gas producer teamed up with a Canadian pumping service company and performed a series of Liquified Petroleum Gas (LPG) fracturing treatments in Pennsylvania's Marcellus Shale. A total of four stages were pumped on three different vertical wells in three different counties Centre, Lycoming, and Indiana. Questions surrounding damage associated with pumping large volumes of fresh water in nano-to-micro Darcy shale along with environmental pressures about water supply and disposal led to consideration of an alternative, non-aqueous fracturing fluid. A hydraulic fracturing process utilizing 100% LPG as a fracturing fluid to carry proppant into shale or a coal seam or other water sensitive pay zone was developed. The new LPG fluid could be easily viscosified with oil gelling chemicals allowing for excellent proppant transport. LPG was an appealing choice of fracturing fluid because its phase behavior in the Marcellus Shale at bottomhole conditions would theoretically render it non-damaging. The fracturing process brought sand into the gelled LPG so that it could be pumped under high pressure and into created fracture geometry in a pay zone. With respect to well cleanup post-frac LPG held the promise of greater cleanup and improved effective fracture half-lengths. This paper will provide details about the LPG fracs and the resulting production response using a mix of readily available public domain information and information obtained by service providers or vendors. Initially, it was author's intention to use only public data but changes in the local market made access to more information available. A comparison of LPG fracturing treatments with slickwater treated wells will be shown for two of the three wells. Concluding remarks will discuss the current state of LPG fracturing in North America and a possible explanation for production results that did not meet expectations.

The development of shale gas reservoir has made rapid progress due to the technology advancement in the past decades, which triggered the energy revolution in the United States. Estimating ultimate recovery, the amount of shale gas that can be economically recovered from a reservoir, is an essential task for E&P companies. Based on the estimated ultimate recovery information, a reservoir engineer could determine the economic investment of whether a single well or series of wells would be worthy. Thus, it must be done fast and right. For the past years, the typical methodology of evaluating remaining reserves or estimated ultimate recovery (EUR) of a well or a field is decline curve analysis. This traditional methodology only requires production data for analysis, which is very easy to access and very convenient to use. There are several empirical formulation specifically designed for shale or tight gas reservoir, such as powerlaw exponential decline, stretched exponential decline, doung’s method, etc due to the complexibities of the reservoir nature, flow behavior. However, this curve fitting technique is still problemtic because the reservoir nature itself and the operation performance are not considered into the analysis, which we believe must be a big deal for determination of ultimate recovery. In this work, the data driven analytics, which is deemed as a smart technique, has been implemented in one asset of Marcellus shale. Different from traditional decline curve analysis, data-driven analytics is a data mining and artificial intelligence based technique, which not only considers measured production data, but also takes reservoir characterisitics and completion data into account. Through this research, we are trying to investigate the impact weight of different groups of parameters such as reservoir characteristics, operational activities on the ultimate recovery determination in shale gas reservoir.

The scope of this paper is to use publicly sourced data as a starting point for production analysis of two distinct well populations. The well populations that will be examined are horizontal Marcellus Shale wells (1) frac'd earlier in time with a defined plug-to-plug stage spacing and (2) frac'd at the same time or later in time with 1.5 to 3 times the number of stages, a production acceleration technique known as Reduced Cluster Spacing (RCS). Public production databases were used to create snap shots of completion activity over time. To speed work flow, a more robust and regularly updated commercially available database that contains both completion and production data was used. Two distinct populations of wells for analysis were identified. A conservative decline curve analysis (DCA) was made to forecast estimated ultimate recovery (EUR) for all wells using only public data. For selected wells, reciprocal production rate versus square root of time plots were generated. This paper will attempt to answer the following questions: (1) were statistically meaningful populations of both stimulation techniques found to exist; (2) how much production uplift did RCS achieve both early on and later time; (3) was there a real increase in EUR; (4) was there a difference in stimulation efficiency; and, (5) what can we learn by plotting P10, P50, and P90 values and EUR distribution curves for specific well populations. To the authors' knowledge this would be the first SPE paper to explore this topic over such large Marcellus Shale well populations in Pennsylvania.

Unconventional reservoirs require hydraulically fractured horizontal wells in order to produce reservoir fluids economically. Induced hydraulic fractures interacting with pre-existing natural fractures results in complex fracture networks (CFNs). Even though hydraulic fracture propagation has been investigated extensively, there is indeed a lack of good understanding of characterization approaches for pre-existing natural fractures. This work presents a practical CFN generation approach by incorporating stochastic algorithms, core, and microseismic data. Based on analysis of geological structures and core observation, natural fracture density, length, and strike distributions can be obtained. Fracture length follows a power law distribution constrained by minimum, maximum and cutoff lengths as well as a distribution exponent. Fracture strike follows a Fisher distribution. Then, microseismic event locations are used to constrain fracture centers of stochastically generated natural fractures. Moreover, a fast proxy model is developed for hydraulic fracture propagation, which honors both the total mass volume of the pumped proppants, and the pre-defined reference lengths. Finally, a field case study is used to demonstrate how to apply the proposed fracture generation and simulation workflow to model hydraulically fractured horizontal wells. The proposed workflow implemented stochastic algorithms with the capabilities to incorporate as much information as possible such as core analysis and microseismic information, and to evaluate uncertainties due to pre-existing natural fractures. With the assumption that microseismic event locations are reactivation of pre-existing natural fractures, the perturbed event locations constrained the locations of the natural fracture centers, resulting in a better description of microseismic-derived stimulated reservoir volume. The simplified hydraulic fracture propagation scheme was able to efficiently estimate the resulting complex fracture networks, and to accurately honor the material balance during fracturing treatment. Sensitivity analysis showed that fracture permeability, matrix porosity, and matrix permeability of the CFNs affect well production performance, significantly. This paper discusses how to utilize available data resources to generate representative CFNs for hydraulically fractured horizontal wells. The process of data preparation for each step of the workflow is discussed in details to facilitate engineers to solve practical problems with the developed methodology.

In the current market conditions, foreseeing drilling risks and mitigating all the surprises beforehand will greatly reduce development costs. Rig personnel use the risk and message object in WITSML schema and daily drilling reports to capture rig activity, findings, unfavorable events, 24 hours well summary, etc. All the information from these objects are not extracted and effectively utilized in well planning exercise. This paper focuses on a collaborative approach of using text mining and knowledge management practices to mine unfavorable events and best practices from daily drilling reports and integrating them with subsurface information management system for maintaining risk inventory and Geospatial analysis. The proposed approach applies Information retrieval, Extraction, Clustering, Pattern Identification and Knowledge Management on daily drilling reports from offset wells to identify any data that might be relevant to drilling anomalies and best practices. Text mining converts unstructured information within the reports to structured data that can be then stored within existing subsurface data management systems. The results of text mining can be integrated with Geospatial techniques to perform risk assessment by leveraging spatially distributed large datasets from offset wells. This collaborative approach improves extracting and sharing of the risks and best practices among experts across the globe and alerting users of upcoming risks. This approach also enables building an effective risk inventory that can be reused for future well planning purposes. Using this approach, risk assessment can be executed for every rig activity, user defined depth range, hole section / size and upcoming formation markers. The results from text mining approach can also be visualized on advanced User Interfaces providing a bird's eye view of well operations. This approach streamlines the evaluation of risks in the planning stage of the well and enables effective collaboration between the drilling, geological and geophysical teams during execution. It also improves risk management and knowledge management practices. With the proper training, a well-defined drilling process, sufficient data and tools for interpretation; drilling a well should become a routine process.

Monitoring an appropriate set of performance metrics is an essential part of a reservoir management strategy that seeks to maximize the efficiency of CO 2 -EOR floods, and benchmark the performance of the reservoir to other fields in the region. While a number of studies have outlined various options for evaluating the performance of the reservoir, many are not applicable to depleted oilfields that are commonly encumbered with limited data availability. As interest in CO 2 -EOR grows beyond giving new life to old fields, into a means of reducing anthropogenic CO 2 emissions, metrics that enable the analyst to assess the storage capacity of the reservoir in addition to oil recovery efficiency have also become increasingly desirable. This paper will present a streamlined set of metrics that meet these objectives, along with their implementation in a dashboard-style presentation. These metrics provide complementary and non-redundant information, and crucially, are still applicable where a paucity of pressure data exists. The analyst is equipped with four groups of critical information. These are (A) flow rate and cumulative production data; (B) EOR performance trends (incremental oil recovery efficiency, CO 2 storage efficiency, and net CO 2 utilization factor); (C) operational performance trends (voidage replacement ratio and producing CO 2 -oil ratio); and (D) storage performance trends (incremental oil recovery factor vs. CO 2 storage and CO 2 storage capacity vs. voidage). To facilitate comparison, all metrics are normalized to percentage of original hydrocarbon pore volume (HCPV) injected. The utility of the selected set of metrics is demonstrated with data obtained from CO 2 -EOR activities in depleted oilfields of the Midwest Regional Carbon Sequestration Partnership (MRCSP) region. First, a nine-panel dashboard is populated using the metrics discussed for the D-33 reservoir, and will be shown to be useful in: quantifying CO 2 storage capacity, highlighting reservoir performance parameters (CO 2 breakthrough, oil recovery, voidage-related pressure changes during EOR etc.) and identifying CO 2 -flood maturity. Second, a four-panel dashboard is displayed as a comparative tool across all reservoirs in the region in order to highlight best and worst performing reservoirs and establish representative ranges for reservoir performance. Both dashboards will be useful when comparing performance against averages published in literature. This work's novelty is attributed to presenting an ideal set of performance metrics by streamlining the numerous options available. A unique dashboard-style presentation is suggested as a means of rapidly assessing oil recovery and CO 2 -storage performance, and collectively yielding useful insights.

Twenty years ago geologists involved in the Appalachian Basin's oil and gas industry knew about the Devonian black shale called the Marcellus, its black color made it easy to identify. However, at the time, very few of these geologists were excited about the Marcellus Shale as a major source of natural gas. Wells drilled into the formation produced some gas but rarely in commercially viable volumes. Few if any in the natural gas industry suspected that the Marcellus would soon be a major contributor to the natural gas supply of the United States – large enough to be called a "super giant" gas field. ( King, 2015 ).

In this paper we present a proposed semi-quantitative approach for assessing the integrity of active and retired oil and gas wells in example study areas for siting geologic carbon storage repositories. The approach makes use of readily available wellbore information and is illustrated with study areas in Ohio and Michigan. Results help depict wellbore integrity issues in a region that has numerous oil and gas wells dating back to the 1800s. We first reviewed readily available sources of information and identified three key well condition indicators that might be expected to impact wellbore integrity – wellbore age, depth and status. Each of these indicators was then coded for each available well with a simple scale representing increasing levels of expected wellbore integrity and combined to form a composite integrity score. The resulting composite indicators provide a useful tool to help identify areas where existing wells tend to be newer, shallower, and plugged and abandoned– all of these being conditions more favorable for repository siting. When considered in combination with maps showing the depth of targeted geologic confining layers and maps indicating the density of existing wells (i.e., the number of existing wells per 1 km 2 grid cell), the maps of wellbore integrity provide a more complete evaluation of different areas for repository siting. Study areas in Ohio and Michigan were evaluated and pros and cons of each area are discussed to help illustrate the utility of this proposed wellbore integrity approach. The approach proposed here makes use of reasonably extensive and readily available information, and combines it in a systematic and objective way, creating an intuitive tool to assist with repository siting.

The use of fiber-optic cable and control lines for instrumented well installations poses certain obstacles to companies wanting to safely design and execute perforating programs which do not damage the costly cable and instrument installations in highly deviated or horizontal wellbores ( 1 ). Tri-axial magnetic flux leakage logging tools enable the detection and orientation of fiber-optic cables and control lines so that perforating services may be planned and executed with confidence. This method requires an understanding of the type and configuration of the hardware which was installed downhole to protect the fiber-optic or control line during installation and perforating. Surface test logging of the in-situ hardware ensures detection and orientation of the hardware installed in the well. Information derived from the surface test runs is used to detect, identify, and orient the hardware using well log data, and a report is created that defines the relative bearing of the fiber-optic or control line in the horizontal or highly deviated sections of the well. For instrumented well applications, hardware commonly known as blast protectors provide the key hardware features used for fiber-optic and control line protection during perforating operations. This case study describes the surface testing method, logging and deployment options, analysis software, tool configuration, well log data, hardware examples and report. NOVEL/Additional information: Efficient procedure using high-resolution tri-axial magnetic flux leakage technology with post-processing analysis software designed to accurately and quickly provide orientation of fiber optic and control lines in wells of greater than 20° deviation.

Wellbore integrity is a fundamental aspect of site characterization for storage in areas with existing or abandoned oil and gas wells. The condition of well cement and casing structures determine if a well can sustain optimal hydrostatic pressures and prevent leakage. This is a necessary but underappreciated aspect of assessing potential reservoirs when optimal and sustainable pressure conditions are critical for success. Cement bond logs (CBLs), a tool not generally used efficiently, can represent the cement quality around a wellbore and be used to identify intervals of poor cement integrity. A standardized CBL evaluation tool was created to remove inconsistencies in interpretations and used to evaluate 394 CBLs in Michigan and 306 CBLs in Ohio. 700 CBLs were analyzed for six site specific study areas and reviewed regionally across Michigan and Ohio to identify any trends in cement quality and highlight possible fluid migration pathways through wellbores with poor cement integrity. The CBL evaluation tool uses a quantitative bond index method to assign percent bond to wireline log readings based on user defined conditions. Once analyzed, the tool outputs a descriptive statistical dataset. Industry standards were used to categorize wells based on cement quality. Correlations between cement quality and the year and season the well was completed, well depth, cement column thickness, and formations cemented over were explored using basic statistics and graphing methods. As well, a standardized assessment of such a variety of wells and cement conditions allows for the incorporation of these wellbores into subsurface models to identify possible zones of weakness for ideal pressure conditions. There were no strong correlations on the regional scale between cement quality and year/season well was completed, depth of the well, and cement column thickness. The tool was very useful in providing red flags in the assessment of a site specific study area and in the evaluation of potential confining layers. Within a study area it was easy to identify wells with poor cement quality and determine if the wells posed a risk to a potential storage site. High risk areas for potential confining layers were also mapped and could be used as a guide to determine areas of poor cement bond within a specific formation.

In an effort to better understand the well performance in one of the Chevron's assets in San Joaquin Valley, a study was conducted to evaluate the perforation strategies and capture best practices. Well completion through perforation is typically performed using bare essential technology such as wireline logs and perforation guns. For basic reservoir formations simple rules-of-thumb are used for perforation spacing and interval lengths. These are rarely validated by other methods, such as production logging and micro-seismic monitoring. For more challenging lithology, a more appropriate approach would be to place perforation clusters in target formations with similar properties. The research paper presents an efficient use of fuzzy clustering technology for identification of optimum perforation strategy in a challenging waterflood diatomite reservoir. The methodology was applied on all newly drilled wells in the reservoir (within the last two years) and found that this new approach improved our understanding over previous practices, not only by designing optimum perforations but also an increased production was observed. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other (in some sense or another) than to those in other clusters. There are two commonly used types of clustering methods: hard and fuzzy clustering. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels. The fuzzy clustering algorithm, also known as Fuzzy C-Mean (FCM) algorithm, was applied to log data of wells from different areas of a reservoir. Based on the clustering results, the workflow then identified whether the perforation was performed on "good" regions (sand) or on "bad" regions (shale bedding). This information allowed the evaluation of perforation jobs executed and allowed capturing best practices and design changes for future well completions. The case study presents a simple, yet efficient workflow to extract additional information from logs and improve completion strategies and perforation design. The methodology is flexible and can be applied to any well where complex lithology creates a challenge in defining the optimum perforation intervals.

It is estimated that only one third of the remaining worldwide oil and gas reserves are conventional, the remainder being in unconventional reservoirs whose evaluation requires appropriate measurements delivered in a cost-effective way. In the case of shales and other tight reservoirs, the defining characteristics are low matrix porosity and low or ultra-low permeability which requires artificial stimulation to encourage production. The optimum stimulation strategy for a particular reservoir is strongly dependent on the distribution of organic material, and on the mechanical and geometrical properties of the rock, and the associated stress field. It is essential to quantify these to an appropriate level of certainty, and well logs are the primary source of such data. Until recently the options for acquiring appropriate logs in high angle and horizontal wells have been constrained either by the limited available sensors or tool conveyance methods. However, the introduction of memory capable small diameter specialized tools and multiple innovative conveyance options has changed the cost-benefit balance for the better. This paper reviews the current status of open hole log measurements with full spectrum conveyance options, and how they impact the evaluation of these challenging reservoirs.

This project was undertaken to evaluate well potential and completion effectiveness for hydraulically fractured horizontal Marcellus completions located in Susquehanna County, Pennsylvania. This paper summarizes a study of the response of the Marcellus shale to hydraulic fractures and identifies performance drivers. How effective are these completions? How would these wells produce if they were completed and fraced differently? What are the primary controllable production drivers? How significant is geology and reservoir characteristics on well production? This paper attempts to answer such questions. Identification of major performance drivers becomes important in the design and optimization of new completions. They are not just important in enhancing production response and ultimate recoverable reserves but also prove to be important economic factors in new completion design. This study employs neural network (ANN) modeling techniques to develop a predictive model to identify performance drivers and evaluate completion effectiveness. Sensitivities performed on the predictive ANN model developed for this project, indicate that well to well variation in reservoir quality and geology has a dominate effect on Marcellus production. Issues such as fracture spacing, frac volume, perforation distribution, proppant amount and fluid volume also affect well production. A summary of completion and frac methodology for wells in this database and a ranking of controllable (Completion and Frac) and non-controllable (Reservoir and Geology) parameters that effect Marcellus production are included. This information will be useful to stake holders interested in identifying reservoir, completion and frac parameters affecting production from the Marcellus and other analogous shale.

Shale gas reservoirs are quickly becoming an important source for natural gas. Historically, the shale basins were not looked at for economic production because of their very low permeability values. However, with the use of hydraulic fracturing combined with horizontal well completions, shale plays have become economically producible, especially the Marcellus shale. In order for these wells to produce, hydraulic fracturing must be employed. This technique makes production viable, but it also changes the in-situ stress field, through the fracture itself and the increased production that occurs. This change in the stress field can damage the wellbore and compromise the integrity of the casing. In this study, geomechanic and reservoir models were constructed using commercially available software to study the change of the stress field in the formation. The parameters of the hydraulic fracture were varied to study the effects of the hydraulic fracture half length and the fracture permeability on the stress field, specifically near the wellbore.

The interest in exploitation of ultra-low permeability formations, such as the Marcellus Shale, has increased in the recent years. Shale formations require massive stimulation treatments to achieve economic production. The recent advances in horizontal drilling and multistage hydraulic fracturing have proved successful in achieving commercial production. However, the parameters that directly affect lifetime production of the wells have not been well established. The primary objective of this study is to examine the effects of basic stimulation parameters used in hydraulic fracturing of Marcellus Shale wells on production performance. Historical production data and stimulation treatment information have been collected and analyzed for a number of horizontal wells both in West Virginia and Pennsylvania. A commercial reservoir simulator which utilizes a dual porosity model and accounts for adsorbed gas was utilized for history matching and predicting the long term production performance. The impact of the stimulation parameters including number of stages, stage spacing, the volume of water used, and the volume of sand used on production performance was investigated. An understanding of the impact of each stimulation parameter to overall production of a well can be used for the optimization of stimulation treatments.

Horizontal well performance in shale gas plays is highly dependent on placing the well in the preferred target zone of the reservoir interval. Some operators who drill in shale plays do not have seismic data and depth structural maps. They plan their horizontal wells with only offset well data. Sometimes, their wells land outside of the preferred target because they blindly place horizontal wells by using only non-directional gamma ray (GR) log data. For optimal well planning and placement for optimized production, it is important to minimize uncertainty about reservoir structure and placement. Usually, operators create a depth map from identified markers such as well tops correlated across available wells in the field. The generated map is limited and often leads to landing laterals outside the target zone, especially when well control is not ideal. The aim of this paper is to illustrate a method of building structural maps and 3D geologic models for drilling in shale gas reservoirs. This methodology lends itself particularly well (but not exclusively) to the pad drilling approach common in shale gas development. The use of logging-while-drilling (LWD) technology can be strategically planned from pad to pad to help mitigate structural uncertainty. Data from 42 horizontal and 6 vertical wells in the project area allowed real-time density image and azimuthal logs to be coupled with vertical offset well information to model the structure along the drilled horizontal well trajectory. The model integrated all the well tops and real-time bedding dip information to produce modeled surfaces. The structural model generated during this study can be used for new well landing-point planning with limited true vertical depth uncertainty and for other fieldwide reservoir characterization. If structure information becomes available from seismic data, this technique also can be used to update the structural model derived from seismic interpretation, thereby delivering enhanced structural control of the field.