Petro-steering methodologies during under balanced coiled tubing (UBTC) drilling operations

A system and methods for petro-steering methodologies are provided. An exemplary method obtains rock fabric data, and integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. Gas rates and steering values are predicted across UBCT wells based on the patterns.

TECHNICAL FIELD

This disclosure generally relates to hydrocarbon exploitation involving, for example, heterogeneous reservoirs.

BACKGROUND

In exploiting hydrocarbon in heterogeneous reservoirs, underbalanced coiled tubing drilling (UBCTD) enables a cost effective solution to increase and sustain production. Generally, rock properties vary with location in heterogeneous reservoirs. Rate of penetration (ROP) and instantaneous productivity index (PI) information is used as input to a proxy for drilling across good reservoir quality. Relying solely on an ROP and PI proxy results in an increased uncertainty on lateral contribution and gas gain as outputs are not linked with operational changes and geological description.

SUMMARY

An embodiment described herein provides a computer-implemented method for petro-steering. In embodiments, rock fabric data is obtained. The rock fabric data is integrated with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. Gas rates are predicted in an underbalanced coiled tubing drilling wells based on the patterns.

An embodiment described herein provides a system for petro-steering. In embodiments, the system includes one or more memory modules and one or more hardware processors communicably coupled to the one or more memory modules. The one or more hardware processors are configured to execute instructions stored on the one or more memory models to perform operations comprising obtaining rock fabric data. The operations integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. The operations predict gas rates in an underbalanced coiled tubing drilling wells based on the patterns.

An embodiment described herein provides an apparatus for petro-steering. In embodiments, the apparatus includes non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising obtaining rock fabric data. The operations integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. The operations predict gas rates in an underbalanced coiled tubing drilling wells based on the patterns.

DETAILED DESCRIPTION

Generally, heterogeneous reservoirs have mineralogy, organic content, natural fractures, and other properties that vary from place to place. This heterogeneity makes it critical to drill laterals across the best reservoir developments to maximize production. During underbalanced coiled tubing drilling (UBCTD) operations, reservoir engineers receive large amounts of data from different sources such as drilling parameters, well-testing data, bio-steering data, lithological cutting description, and geological inputs. This type of information can be provided by different disciplines using different formats and structures. The variation of formats and structures can make decision-making inefficient as engineers are unable to process and analyze all data generated during drilling operations. As a result, engineers may rely solely on rate of penetration (ROP) and instantaneous productivity index (PI) information as input to a proxy for drilling across good reservoir quality.

Generally, Under Balanced Coiled Tubing Drilling (UBCTD) technology involves drilling a well with fluid pressure lower than the reservoir pressure. Due to the underbalanced condition imposed in the wellbore, the well is allowed to flow naturally during drilling, while its productivity is measured in terms of gas rates and pressures. In embodiments, improved productivity and production sustainability in UBCTD wells is enabled by maximizing the effective reservoir contact in the drilled laterals.

In some cases, barren reservoirs lack good biostratigraphic control. Such barren reservoirs are common in Paleozoic aged hydrocarbon bearing sandstones in, for example, eastern Saudi Arabia. Other biostratigraphically barren successions are found globally. These barren reservoirs are generally devoid of age diagnostic microfossils and palynomorphs, meaning that biostratigraphy cannot be utilized to identify particular stratigraphic beds, including reservoirs. As a result, biostratigraphy techniques cannot be utilized to navigate laterals (e.g., steer) across barren hydrocarbon bearing formations due to the absence of palynomorphs or microfossils. Deployment of UBCTD technology in this type of reservoir generally yields low productivity wells.

Embodiments described herein enable petro-steering methodologies during under balanced coiled tubing (UBTC) drilling operations. In examples, a petro-steering methodology is described that identifies the most productive layers of a hydrocarbon bearing sandstone reservoir in near-real time while drilling UBCTD laterals. In embodiments, near-real time refers to the time it takes for the samples to reach a laboratory (e.g., a laboratory at or near the wellsite) after being drilled and subjected to analysis, including time spent on sample preparation and analysis before the results are available for interpretation. Rock fabric data is integrated with dynamic productivity data to support geosteering of UBCTD laterals in sandstone reservoirs. Generally, rock fabric data describes the mineralogy and geochemistry of the sample. Rock fabric data includes, for example, petrography and geochemical analysis using X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF). In embodiments, productivity data refers to recorded gas rates and bottom-hole drawdown pressures (ΔP) which are measured during drilling. Productivity Index (PI) is calculated by dividing the gas rate with the ΔP.

For ease of description, the present techniques are described using an exemplary reservoir with three gas wells and twelve UBCTD laterals. However, the present techniques are applicable to a reservoir with any number of gas wells and any number of laterals. In the exemplary reservoir described herein, drilling cuttings are collected regularly every 30 feet (ft) for analysis. Generally, tests applied to the drilling cuttings includes petrography, XRD, and XRF tests on more than 200 drill cuttings.

Referring toFIG.1A, an example drilling operation100A is presented to illustrate a lateral configuration for UBCTD according to an implementation of the present disclosure. A wellbore102refers to a vertical well. Wellbore102may correspond to a motherbore. Drilling operations can include sidetracking one or more laterals from the motherbore to explore reservoir in the horizontal direction. As illustrated, lateral104A refers to a first window at 9,097 ft measured depth (MD) as a first side track. The lateral104A reaches a measured depth (MD) of 10,875 ft. Lateral104B refers to a second window at 8,960 ft measured depth (MD) as a second side track. The lateral104B reaches a measured depth (MD) of 9,748 ft. Lateral104C refers to a third window at 8,936 ft measured depth (MD) as a third side track. The lateral104C reaches a measured depth (MD) of 11,500 ft.

In examples, drilling cuttings were obtained during drilling operation at a wellbore such as wellbore102. For example, during drilling operations cutting samples are collected, labeled and transported to laboratory facilities where they were analyzed. Three different testing techniques are used to generate rock fabric data: Petrography, X-Ray diffraction (XRD) and X-Ray Fluorescence (XRF). Generally, petrography provides information on the minerals present in individual samples and the character of authigenic minerals and textual components. XRD determines the presence and quantities of specific minerals, and XRF acquires inorganic geochemical data for up to forty elements in the range Na—U in the periodic table.

To use machine learning to identify the elements, ratios, and minerals (data acquired by XRF and XRD) that are most suitable to predict gas flow. In examples, more than two hundred cuttings samples were subjected to petrographic, XRD and XRF analysis. The results were reviewed by subject matter experts (SME) to check data quality prior to interpretation. Over 300 different geochemical parameters, including both individual elements generated by XRF and elemental ratios were examined. For example, the individual elements and elemental ratios include a Silicone Oxide/Aluminum Oxide (Si/Al) ratio, Potassium Oxide (K2O), Potassium (K), Uranium (U) and a Zirconium/Aluminum Oxide (Zr/Al) ratio as illustrated in the plot100B ofFIG.1B.

In the examples described herein, individual mineral profiles were plotted for each well of the three wells. In particular, cutting samples were processed and analyzed by XRD, in the form of whole rock analysis to identify minerals. For example, mineralogical analysis is executed on randomly oriented powders using a powder X-Ray Diffractometer with Cuka radiation (40 kV, 40 mA), in the 3°-100° (2θ) interval with a step size of 0.02° increment. The XRD patterns are interpreted with a classifier or using cluster analysis to obtain profiles of individual minerals as illustrated in the plot100C ofFIG.1C. In examples, ditch cuttings were subjected to petrographic analysis in order to determine the percentages of the most abundant minerals and lithic fragments to obtain rock fabric data. Additionally, grain size, sorting, roundness, and occurrence of quartz overgrowths were recorded as rock fabric data.

Results from the petrography, XRD, and XRF are integrated with UBCTD productivity data and drilling data to identify patterns between petrography, geochemistry, mineralogy, lithology description and well dynamic performance to be used as proxy for UBCTD lateral placement across “sweet spots” of a hydrocarbon bearing sandstone reservoirs. In some cases, patterns are associations between geochemistry, mineralogy and gas flow rates. The most useful elements and elemental ratios are identified in order to predict such rates. Generally, to integrate of rock fabric data with dynamic productivity data, the dynamic productivity data and rock fabric data (i.e. XRF, XRD, petrography) are plotted in profile form in tracks beside each other as well as in the form of crossplots as illustrated inFIG.1BandFIG.1C. Generally, the dynamic productivity data includes a gas rate at a surface of the well, surface and bottom-hole pressure measurement, choke size, and an artificial gas lift. The drilling data includes rate of penetration, footage drilled, gamma-ray response, inclination and azimuth. In examples, the laboratory results were integrated with dynamic productivity data and drilling data, and analyzed using advanced statistical tools along with machine learning algorithms to evaluate correlation between more than 150 variables: including lab results from XRF, XRD and petrography. As a result of this analysis, several geochemical proxies were correlated with gas-gain/higher productivity and therefore can be used to identify productive intervals in hydrocarbon bearing sandstones. Accordingly, the present techniques predict gas rates and steering within the most productive parts of a tight gas bearing permo-carboniferous sandstone reservoir. Post-flowback analyses have shown significant improvement in the well deliverability.

In embodiments, the dynamic (i.e. productivity data generated during drilling such as pressures and gas rates) and static data (i.e. geochemical data generated by XRD and mineralogical data acquired by XRD) is cleaned, reviewed and integrated, with a selection of benchmark variables generated. Variables include: instantaneous PI (Equation 1), Cumulative PI (Equation 2) and Normalized PI (Equation 4) and first derivative of Normalised PI (Equation 5).FIG.2illustrates gas rate and gas gain logic used for benchmarking variable selection.

FIG.2illustrates gas rate and gas gain logic used for benchmarking variable selection. Generally, a gas gain results from an increase in gas rates. In embodiments, advanced data analytics techniques enable an understanding of data relationships capitalizing on advanced statistical methodologies (i.e. Exploratory Data Analysis) and AI techniques (i.e. machine learning) to benchmark and predict reference variables (i.e. gas gain).

In the example ofFIG.2, a decrease in bottom hole pressure (BHP) correlates to in an increase in differential reservoir pressure, which correlates to an increase in gas rate. The corresponding productivity index remains the same. In the example ofFIG.2, an increase in gas rate correlates to an increase in the productivity index when the differential reservoir pressure is the same. Additionally, in the example ofFIG.2, an increase in bottom hole pressure correlates to a decrease in differential reservoir pressure, which correlates to an increase in gas rate and an increase in the productivity index. The wellhead pressure will increase as well.

Multi-variable analysis is executed to identify the best parameter to benchmark well performance with laboratory results. The analysis shows that the best variable to be used for dynamic-laboratory correlation is the Normalized PI's first derivative. As shown inFIG.3, when a lateral well penetrates productive intervals, the first derivative of normalized PI increases. In examples, a productive interval is a “sweet spot” for drilling. On the other hand, when a poor quality reservoir is drilled, the normalized PI first derivative decreases towards zero.

Exploratory Data Analysis (EDA) is implemented to determine relationships between laboratory tests (Petrography, XRD and XRF) and Normalized PI's first derivative. In order to establish whether differences or associations exist between datasets, inferential statistical analysis is utilized. Each lab output result is compared with a benchmark variable to establish whether the drilled interval contributed to the production. Samples are split into two groups depending on dynamic performance at the collected sample depth, i.e. if the slope of NPIFD is close to zero at sample depth, the sample was assigned to No_Gas_Gain group (plotted as black X's inFIGS.4-6), otherwise the sample was assigned to Gas_Gain group (plotted as grey, filled O's inFIGS.4-6)

The paired plots ofFIG.4illustrate a summary of inferential statistical analysis of XRF results (e.g., rock fabric data). The plots400visualize how variables correlate/differ among themselves. The diagonal plots402show dataset population distribution between the two families (gas-gain and no-gas-gain). In general, if both population datasets overlap, there is association between properties. On the other hand, if there is a separation, an inference is made that the variable can be used a predictor to determine the gas gain. Measurements with a high correlation are highlighted with a black box. For example, the Si/Al gas-gain median is different from no-gas-gain population meaning that this ratio can be used as a parameter to identify favorable productivity intervals. Similar behavior was observed for K, Zr/Al and U. Some of the petrography measurements inFIG.5are subjective values such roundness, sorting, gain size and quartz overgrowths, making their identification more challenging. Overall, petrography results ofFIG.5show a moderate correlation with properties such as grain roundness, free quartz content and quartz overgrowth with dynamic data. On the other hand, XRD inFIG.6did not show any correlation to the gas-gain.

In examples, more than 300 variables were reported from all tests. In order to process and select the most important variables for identification of productive intervals, a machine-learning (ML) model is trained. The objective of training the machine learning model is to predict gas gain (Normalized PI first derivative), based on rock fabric data. In embodiments, the machine learning model reduces the number of variables to be used as proxies to identify high productive layers. As illustrated inFIG.7, variable input is reduced from 300 parameters to seven parameters. Those parameters were selected to be validated with dynamic performance from the laterals.

FIG.8is an illustration of exemplary visual results that predict gas rates and steering values. Traditionally, a high ROP was used as a proxy for soft rock, which implies high porosity/permeability and gas gain. It is highlighted by circles810and812, where high ROP values are reported across no gas gain intervals (no increases on Normalized PI). Potentially there is uncertainty on gas-gain, however, due to operational factors such as overbalance pressure, wellbore stability or sub-hydrostatic reservoir pressure. In the example ofFIG.8, the well did not experience any of those factors.

The plots ofFIG.9compare dynamic productivity data such as Gas Rate, ΔP (Reservoir pressure−flowing borehole pressure), and Norm_PI in tracks 1-3 respectively with most relevant petrography parameters such as roundness, Free Quartz Grains (FQG) and quartz overgrowth in tracks 4-6 respectively. Laterals are from the same exemplary well. Arrows906and908highlight intervals where high production was encountered. In first lateral902, a good correlation is observed between high roundness index (diagonal shaded areas910and912) and high free quartz grains (FQG) across the highly productive interval. The trends are not, however, consistent across all laterals as illustrated in the Lateral_3 plot904. As a result, petrography and XRD parameters were excluded for identifying highly productive zones.

The plots inFIG.10compare dynamic productivity data such as gas rate, ΔP (Reservoir pressure−Flowing Borehole pressure, and Norm_PI in tracks 1-3 respectively with the most relevant XRF measured elements/ratios such as Si/Al, K, U and Zr/Al in tracks 4-7 respectively. Laterals1002and1004are from the same exemplary well. Arrows1006and1008highlight the penetration of high productive intervals. The following is a brief description of the selected XRF elements.

Silicone Oxide/Aluminum Oxide ratio (Si/Al):Si is mainly present in quartz, Al occurs in clay minerals, thus high Si/Al ratios are an indication of quartz dominated intervals with low clay contents.

Potassium Oxide K2O is an element predominately present in clays such as illite. Through the pilot project, we observed that a small increment on clay content could have a detrimental effect on PI.

Uranium: low values in Uranium are related to low presence of clay material.

Zirconium/Aluminum Oxide ratio (Zr/Al) is related to Al-bearing clay minerals.

Results show that there is an excellent correlation between the high productive intervals (additional gas-gain) and high Si/Al ratios with low clay mineral contents.

In the examples described herein, after initial drilling operations, two additional gas wells were drilled across the sandstone formations using petro-steering the results of which are shown inFIG.11. After deploying petro-steering as described herein, wellhead PI increased by three-fold compare with historical UBCTD wells geosteered with conventional techniques as illustrated inFIG.12. Accordingly, the present techniques use rock fabric data results measured in near-real time with a combination of dynamic productivity data to identify “sweet spot” intervals. This will improve reservoir contact and consequently well deliverability.

FIG.13is a process flow diagram of a method for petro-steering as described herein. At block1302, rock fabric data is obtained. In examples, rock fabric data includes data obtained using petrography, and geochemical analysis using XRD and XFD data. In examples, during drilling operations cutting samples are collected, labeled and transported to laboratory facilities where they are analyzed. At block1304, rock fabric data is integrated with dynamic productivity data (e.g., UBCTD well testing and drilling data) to identify patterns between petrography, geochemistry, mineralogy, lithology description and well dynamic performance. In embodiments, patterns are identified by comparing gas rates, ΔP and Norm PI with geochemical and mineralogical data. Each rock fabric dataset can be plotted in profile track and in the form of crossplots.

At block1306, gas rates and steering across UBCTD wells is predicted based on the patterns between petrography, geochemistry, mineralogy, lithology description and well dynamic performance. In particular, the predicted gas rates and steering values are used to guide petro-steering during drilling operations to locations that are predicted to have a high productivity (where productivity is measured in terms of gas rates and pressures). In embodiments, the gas rates and steering values generate UBCTD laterals across “sweet spots” in sandstone reservoirs. Accordingly, the petro-steering workflow as described herein integrates XRF analysis and XRD analysis with dynamic performance in real-time for UBCTD wells across biostratigraphy barren formations. This data integration maximizes reservoir contact and improved decision-making process while drilling UBCTD wells.

FIG.14is a block diagram of an example computer system1400used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure for petro-steering methodologies, according to some implementations of the present disclosure.

The illustrated computer1402is intended to encompass any computing device such as a server, a desktop computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer1402can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer1402can include output devices that can convey information associated with the operation of the computer1402. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI).

The computer1402can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer1402is communicably coupled with a network1440. In some implementations, one or more components of the computer1402can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.

The computer1402can receive requests over network1440from a client application (for example, executing on another computer1402). The computer1402can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer1402from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.

Each of the components of the computer1402can communicate using a system bus1408. In some implementations, any or all of the components of the computer1402, including hardware or software components, can interface with each other or the interface1404(or a combination of both), over the system bus1408. Interfaces can use an application programming interface (API)1416, a service layer1418, or a combination of the API1416and service layer1418. The API1416can include specifications for routines, data structures, and object classes. The API1416can be either computer-language independent or dependent. The API1416can refer to a complete interface, a single function, or a set of APIs.

The service layer1418can provide software services to the computer1402and other components (whether illustrated or not) that are communicably coupled to the computer1402. The functionality of the computer1402can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer1418, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer1402, in alternative implementations, the API1416or the service layer1418can be stand-alone components in relation to other components of the computer1402and other components communicably coupled to the computer1402. Moreover, any or all parts of the API1416or the service layer1418can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.

The computer1402includes an interface1404. Although illustrated as a single interface1404inFIG.14, two or more interfaces1404can be used according to particular needs, desires, or particular implementations of the computer1402and the described functionality. The interface1404can be used by the computer1402for communicating with other systems that are connected to the network1440(whether illustrated or not) in a distributed environment. Generally, the interface1404can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network1440. More specifically, the interface1404can include software supporting one or more communication protocols associated with communications. As such, the network1440or the interface's hardware can be operable to communicate physical signals within and outside of the illustrated computer1402.

The computer1402includes a processor1410. Although illustrated as a single processor1410inFIG.14, two or more processors1405can be used according to particular needs, desires, or particular implementations of the computer1402and the described functionality. Generally, the processor1410can execute instructions and can manipulate data to perform the operations of the computer1402, including operations using algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure.

The computer1402also includes a database1406that can hold data, including seismic data1416and rock fabric data1430(e.g. petrographic, XRD and XRF analysis results), for the computer1402and other components connected to the network1440(whether illustrated or not). For example, database1406can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database1406can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer1402and the described functionality. Although illustrated as a single database1406inFIG.14, two or more databases (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer1402and the described functionality. While database1406is illustrated as an internal component of the computer1402, in alternative implementations, database1406can be external to the computer1402.

The computer1402also includes a memory1412that can hold data for the computer1402or a combination of components connected to the network1440(whether illustrated or not). Memory1412can store any data consistent with the present disclosure. In some implementations, memory1412can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer1402and the described functionality. Although illustrated as a single memory1412inFIG.14, two or more memories1412(of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer1402and the described functionality. While memory1412is illustrated as an internal component of the computer1402, in alternative implementations, memory1412can be external to the computer1402.

The application1414can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer1402and the described functionality. For example, application1414can serve as one or more components, modules, or applications. Further, although illustrated as a single application1414, the application1414can be implemented as multiple applications1414on the computer1402. In addition, although illustrated as internal to the computer1402, in alternative implementations, the application1414can be external to the computer1402.

The computer1402can also include a power supply1420. The power supply1420can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply1420can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply1420can include a power plug to allow the computer1402to be plugged into a wall socket or a power source to, for example, power the computer1402or recharge a rechargeable battery.

There can be any number of computers1402associated with, or external to, a computer system containing computer1402, with each computer1402communicating over network1440. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer1402and one user can use multiple computers1402.

The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship. Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.

Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, some processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results.